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"Berdasarkan Global Cancer Observatory (GLOBOCAN), tumor otak diestimasikan berada di urutan ke-19 sebagai tumor yang paling umum terjadi dan ke-12 sebagai penyebab utama kematian akibat kanker di dunia pada tahun 2020. Walaupun begitu, informasi terkait epidemiologi tumor otak di Indonesia masih sangat terbatas. Belum diwajibkannya pendataan kasus tumor di Indonesia merupakan salah satu alasannya. Tumor otak dapat dideteksi menggunakan pencitraan medis, seperti computed tomography (CT) scan dan magnetic resonance imaging (MRI). Deteksi dini tumor otak merupakan hal yang penting karena dapat meningkatkan tingkat keberlangsungan hidup dari pasien. Saat ini, banyak perkembangan teknologi yang dapat dimanfaatkan untuk membantu kehidupan manusia, salah satunya adalah deep learning (DL). Akan tetapi, data medis merupakan data yang sensitif, sehingga menjadi salah satu tantangan dalam penerapan DL di bidang kesehatan. Untuk mengatasi privasi dan keterbatasan data, terdapat metode federated learning (FL) yang memungkinkan untuk dilakukannya pelatihan data lokal pada klien tanpa menyebarkan data klien tersebut. Pada penelitian ini, akan dibentuk simulasi klasifikasi tumor otak menggunakan DL berbasis FL. Tujuan utama dari penelitian ini adalah untuk menganalisis performa model yang dihasilkan dari federated learning dan membandingkannya dengan metode training konvensional. Terdapat empat cycle data dengan tiga cycle berasal dari dataset pertama (M. Nickparvar) dan satu cycle dari dataset kedua (J. Cheng). Hasil akurasi dan F1-score tertinggi dari simulasi federated didapatkan pada epoch (jumlah putaran pelatihan data pada tiap klien) 15 dan round (jumlah putaran mulai dari tahap pembagian parameter model global kepada klien sampai dengan agregasi model) 15, yaitu 0.8375 dan 0.8384 (cycle 1), 0.7625 dan 0.7567 (cycle 2), 0.8375 dan 0.8308 (cycle 3), serta 0.7333 dan 0.7255 (dataset 2). Hasil akurasi dan F1-score tertinggi dari simulasi standard pelatihan lokal pada tiap cycle adalah 0.75 dan 0.7568 (cycle 1), 0.6875 dan 0.6677 (cycle 2), 0.675 dan 0.6744 (cycle 3), serta 0.7222 dan 0.7085 (dataset 2). Hasil akurasi dan F1-score tertinggi dari simulasi standard pelatihan all data pada tiap cycle adalah 0.7625 dan 0.7644 (cycle 1), 0.6875 dan 0.6723 (cycle 2), 0.775 dan 0.7766 (cycle 3), serta 0.6 dan 0.5355 (dataset 2). Berdasarkan pengujian hasil simulasi, korelasi epoch dan round terhadap performa model signifikan pada dataset kedua (Pacc-epoch = 0.019; PF1-epoch = 0.006; Pacc-round = 0.008; PF1-round = 0.025), tetapi hanya korelasi round yang signifikan pada dataset pertama (cycle 1 Pacc-round < 0.001 dan PF1-round < 0.001; cycle 2 Pacc-round = 0.004 dan PF1-round = 0.003; cycle 3 Pacc-round < 0.001 dan PF1-round < 0.001). Selain itu, performa model global hasil federated learning lebih baik daripada performa model lokal dan model pelatihan standard. Tidak ditemukan perbedaan signifikan antara performa model dataset pertama dengan cycle yang berbeda (Pacc between cycles = 0.679; PF1 between cycles = 0.770) serta tidak ditemukan juga perbedaan signifikan antara performa model dataset pertama dan kedua (Pacc cycle 1-dataset 2 = 0.103; Pacc cycle 2-dataset 2 = 0.334; Pacc cycle 3-dataset 2 = 0.103; PF1 cycle 1-dataset 2 = 0.140; PF1 cycle 2-dataset 2 = 0.120; Pacc cycle 3-dataset 2 = 0.140).

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Based on the Global Cancer Observatory (GLOBOCAN), brain tumors are estimated to rank 19th among the most common tumors and 12th as the leading cause of cancer-related deaths worldwide in 2020. However, information regarding the epidemiology of brain tumors in Indonesia remains very limited. One reason is that case registration for tumors is not yet mandatory in Indonesia. Brain tumors can be detected using medical imaging, such as computed tomography (CT) scan and magnetic resonance imaging (MRI). Early detection of brain tumors is crucial as it can improve the survival rates of patients. Currently, many technological advancements can be utilized to aid human life, one of which is deep learning (DL). However, medical data is sensitive, presenting a challenge in applying DL in healthcare. To address privacy and data limitation problems, there is a method called federated learning (FL) that enables local data training on clients without sharing the clients' data. This study aims to simulate brain tumor classification using DL based on FL. The main objective of this research is to analyze the performance of the model generated from federated learning and compare it with conventional training methods. There are four data cycles, with three cycles from the first dataset (M. Nickparvar) and one cycle from the second dataset (J. Cheng). The highest accuracy and F1-score from the federated simulation were achieved at epoch (number of training rounds on each client) 15 and round (number of rounds starting from the global model parameter distribution to the clients until model aggregation) 15, which are 0.8375 and 0.8384 (cycle 1), 0.7625 and 0.7567 (cycle 2), 0.8375 and 0.8308 (cycle 3), and 0.7333 and 0.7255 (dataset 2). The highest accuracy and F1-score from the standard local training simulation in each cycle are 0.75 and 0.7568 (cycle 1), 0.6875 and 0.6677 (cycle 2), 0.675 and 0.6744 (cycle 3), and 0.7222 and 0.7085 (dataset 2). The highest accuracy and F1-score from the standard all data training simulation in each cycle are 0.7625 and 0.7644 (cycle 1), 0.6875 and 0.6723 (cycle 2), 0.775 and 0.7766 (cycle 3), and 0.6 and 0.5355 (dataset 2). Based on the simulation, the correlation between epoch and round on model performance is significant in the second dataset (Pacc-epoch = 0.019; PF1-epoch = 0.006; Pacc-round = 0.008; PF1-round = 0.025), but only the round correlation is significant in the first dataset (cycle 1 Pacc-round < 0.001 and PF1-round < 0.001; cycle 2 Pacc-round = 0.004 and PF1-round = 0.003; cycle 3 Pacc-round < 0.001 and PF1-round < 0.001). Moreover, the performance of the global model resulting from federated learning is better than the local model performance and standard all data training model performance. No significant difference was found between the performance of the first dataset with different cycles (Pacc between cycles = 0.679; PF1 between cycles = 0.770) nor between the performance of the first and second datasets (Pacc cycle 1-dataset 2 = 0.103; Pacc cycle 2-dataset 2 = 0.334; Pacc cycle 3-dataset 2 = 0.103; PF1 cycle 1-dataset 2 = 0.140; PF1 cycle 2-dataset 2 = 0.120; Pacc cycle 3-dataset 2 = 0.140)."

"Kemunculan suatu penyakit merupakan masalah yang tak terhindarkan di seluruh dunia, termasuk di Indonesia. Tumor otak merupakan salah satu penyakit berbahaya yang dapat menyebabkan kematian. Salah satu jenis penyakit tumor otak yang paling umum dan mematikan adalah glioblastoma. Penderita glioblastoma memiliki tingkat kelangsungan hidup yang cukup rendah dan umumnya didiagnosis pada saat tumor sudah berkembang lebih jauh. Oleh karena itu, sangat penting dilakukan diagnosis secara dini dengan hasil yang akurat untuk menentukan apakah seseorang menderita glioblastoma atau tidak. Pada penelitian ini, metode machine learning, yaitu K-Nearest Neighbor dan Support Vector Machine dengan seleksi fitur Genetic Algorithm (KNN-GA dan SVM-GA) diterapkan dan dibandingkan untuk mengklasifikasi glioblastoma. Genetic Algorithm (GA) diimplementasikan sebagai seleksi fitur untuk menentukan fitur-fitur relevan yang terpilih dan kemudian diklasifikasi dengan metode KNN dan SVM. Data yang digunakan adalah data numerik hasil Magnetic Resonance Imaging (MRI) yang didapat dari RSUPN Dr. Cipto Mangunkusumo. Berdasarkan percobaan yang dilakukan, metode SVM-GA menggunakan kernel Radial Basis Function dan 5 fitur dengan 90% data training adalah metode terbaik untuk mengklasifikasi data glioblastoma. Hasil yang didapat untuk nilai akurasi, recall, presisi, dan f1-score secara berturut-turut adalah 92.35%, 93.19%, 92.62%, dan 92.83%.

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The emergence of a disease is an inevitable problem throughout the world, including in Indonesia. Brain tumor is one of the dangerous diseases that can cause death. One of the most common and deadly types of brain tumor is glioblastoma. Patients with glioblastoma have a fairly low survival rate and are generally diagnosed when the tumor has developed further. Therefore, it is very important to make an early diagnosis with accurate result to determine whether a person has glioblastoma or not. In this study, machine learning methods, namely K-Nearest Neighbor and Support Vector Machine with feature selection Genetic Algorithm (KNN-GA and SVM-GA) were applied and compared to classify glioblastoma. Genetic Algorithm (GA) was implemented as a feature selection to determine the selected relevant features and then classified by KNN and SVM methods. The data used are numerical data obtained from Magnetic Resonance Imaging (MRI) results from Dr. Cipto Mangunkusumo Hospital. Based on the experiments conducted, the SVM-GA method using a Radial Basis Function kernel and 5 features with 90% training data is the best method for classifying glioblastoma. The results obtained for the values of accuracy, recall, precision, and f1-score were 92.35%, 93.19%, 92.62%, and 92.83%, respectively."

"Dalam perkembangan teknologi saat ini, kemampuan mesin untuk dapat belajar memiliki peranan yang sangat penting. Berbagai upaya telah dilakukan untuk mengembangkan kecerdasan buatan terhadap mesin sehingga mesin dapat melakukan pembelajaran. Salah satu macam pembelajaran mesin (machine learning) adalah dengan Brain Emotional Learning (BEL). BEL merupakan metode pembelajaran mesin yang terinspirasi dari fungsi kerja sistem limbik mamalia yang memiliki kemampuan untuk menyimpan memori, membuat keputusan dan memberi respon emosi. Dalam penerapannya, BEL telah terbukti dapat menyelesaikan berbagai masalah pembelajaran, seperti dalam masalah klasisfikasi, masalah prediksi, dan pengendalian. Pada skripsi ini, akan dilakukan perancangan dengan BEL untuk dapat mengkategorikan data melalui metode pembelajaran supervised learning dan diuji dengan data iris.Hasil pengujian menunjukkan bahwa BEL dapat digunakan untuk klasifikasi beberapa macam kelas, terdapat hubungan yang tidak linear dari faktor-faktor yang mempengaruhi proses pembelajaran terhadap hasil, konstanta β dan konstanta γ memberikan hasil akurasi rendah ketika keduanya bernilai besar, dan hasil akurasi terbaik sebesar 93,33% untuk jenis data iris. Selain itu, perbandingan dengan paper rujukan menunjukkan bahwa hasil rancangan memberikan hasil yang lebih baik daripada algoritma GDBP MLP pada epoch rendah meskipun hasil rancangan belum sebaik rujukan.

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In todays technological development, the ability of machines to be able to learn has a very important role. Various efforts have been made to develop artificial intelligence on the machine so that the machine can do learning. One type of machine learning is with Brain Emotional Learning (BEL). BEL is a machine learning method inspired by the work function of the limbic system of mammals that has the ability to store memory, make decisions and give emotional responses. In its application, BEL has been proven to be able to solve various learning problems, such as problems in classification, prediction problems, and control. In this thesis, BEL will be designed to be able to categorize data through supervised learning methods and tested with iris data.The test results show that BEL can be used to classify several types of classes, there is a non-linear relationship of the factors that influence the learning process to results, constants and constants give low accuracy results when both are of great value, and the best accuracy results are 93, 33% for iris data types. In addition, the comparison with the reference paper shows that the design results have better results than the MLP GDBP algorithm at the lower epoch even though the design results have not been as good as the references."

"Penelitian ini bertujuan untuk mengevaluasi perhitungan dosis berdasarkan citra Cone Beam Computed Tomography (CBCT) pada pasien dengan diagnosa tumor otak. Perencanaan dan perhitungan dosis berdasarkan citra CBCT fraksinasi ke-16 yang dilakukan terhadap 13 pasien yang disinari menggunakan pesawat linac Elekta Versa HD dan 7 pasien yang disinari menggunakan pesawat linac Halcyon 2.0. Perencanaan dan perhitungan dosis dilakukan pada Treatment Planning System (TPS) Eclipse dan TPS Monaco. Hasil perhitungan dosis berdasarkan citra CBCT dibandingkan dengan citra Computed Tomography (CT) simulator. Penelitian ini memiliki beberapa tahapan, (1) kalibrasi Hounsfield Unit (HU) citra CBCT menggunakan fantom CIRS CT electron density 062M untuk melakukan perhitungan dosis di TPS dengan nilai HU yang sesuai, (2) proses pengumpulan data citra pasien yang memenuhi kriteria penelitian dan dilanjutkan dengan proses registrasi dan perencanaan citra CBCT, (3) analisis Dose Volume Histogram (DVH) untuk mengevaluasi kualitas perencanaan dengan parameter dosis yaitu Conformity Index (CI) dan Homogeneity Index (HI), (4) analisis dosis Organ at Risk (OAR) terhadap dose-constraint (batas dosis) untuk OAR batang otak, kiasma, sumsum tulang belakang, saraf optik, mata dan lensa. Nilai CI pada perencanaan berdasarkan CT tidak berbeda secara signifikan, Berdasarkan CBCT dari pesawat linac Elekta Versa HD diperoleh CI sebesar 0,05±0,21 (p=0,08) dan -0,01 ± 0,06 (p=0,02) berdasarkan CBCT dari pesawat linac Halcyon 2.0. Sementara itu, nilai HI pada perencanaan berdasarkan CBCT diamati berbeda secara signifikan terhadap CT, Berdasarkan CBCT dari pesawat linac Elekta Versa HD diperoleh HI sebesar 0,25 ± 0,43 (p=0,01) dan 0,08 ± 0,04 (p=0,01) berdasarkan CBCT dari pesawat linac Halcyon 2.0.

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This research aims to evaluate dose calculations based on Cone Beam Computed Tomography (CBCT) images in patients diagnosed with brain tumors. Planning and dose calculations based on the 16th fraction of CBCT images were performed on 13 patients irradiated using Elekta Versa HD linear accelerator and 7 patients irradiated using Halcyon 2.0 linear accelerator. The planning and dose calculations were conducted using the Treatment Planning System (TPS) Eclipse and TPS Monaco. The results of the dose calculations based on CBCT images were compared with the Computed Tomography (CT) simulator images. The research comprised several stages: (1) calibration of Hounsfield Unit (HU) of CBCT images using CIRS CT electron density 062M phantom to perform dose calculations in TPS with appropriate HU values, (2) data collection of patient images meeting the research criteria followed by image registration and CBCT planning, (3) analysis of Dose Volume Histogram (DVH) to evaluate planning quality using dose parameters such as Conformity Index (CI) and Homogeneity Index (HI), (4) analysis of dose to Organs at Risk (OAR) against dose constraints for OARs such as brainstem, chiasm, spinal cord, optic nerves, eyes, and lenses. The CI values for the planning based on CT were not significantly different. Based on CBCT from Elekta Versa HD linear accelerator, the CI obtained was 0.05 ± 0.21 (p=0.08), and based on CBCT from Halcyon 2.0 linear accelerator, the CI obtained was -0.01 ± 0.06 (p=0.02). However, the HI values for planning based on CBCT significantly differed from CT. Based on CBCT from Elekta Versa HD linear accelerator, the HI obtained was 0.25 ± 0.43 (p=0.01), and based on CBCT from Halcyon 2.0 linear accelerator, the HI obtained was 0.08 ± 0.04 (p=0.01)."

"Tumor merupakan benjolan yang dapat terbentuk di bagian tubuh mana pun, disebabkan oleh pertumbuhan sel di dalam dan sekitar jaringan yang tidak normal dan tidak terkendali. Pasien tumor otak akan merasakan emosi seperti syok, agitasi, kemarahan, kesedihan, dan penarikan diri. Penarikan diri muncul karena ketakutan terhadap kemungkinan akibat yang terjadi, seperti perubahan citra tubuh atau kematian sehingga terjasi distress pada pasien tumor otak. Distress adalah pengalaman tidak menyenangkan multifaktorial yang berasal dari berbagai faktor, termasuk aspek psikologis (seperti kognitif, perilaku, dan emosional), aspek sosial, spiritual, dan fisik yang dapat mengganggu kemampuan seseorang untuk mengatasi kanker secara efektif, gejala fisik dan pengobatannya. Salah satu cara untuk menurunkan distress adalah dengan aromaterapi. Penelitian ini bertujuan untuk mengetahui efektifitas aromaterapi terhadap nilai distress pada pasien tumor otak. Penelitian ini menggunakan desain penelitian kuasi eksperimen (quasi experimental) dengan rancangan non- randomized pre-test-post-test with control. Besar sampel pada penelitian ini menggunakan porpusif random sampling yang berjumlah 15 responden kelompok intervensi dan 15 responden kelompok kontrol. Analisa data diperoleh nilai p value = 0.000 (p<0,05) yang artinya terdapat perbedaan yang signifikan nilai distress pada kelompok intervensi dan kelompok kontrol. Pemberian aromaterapi terbukti efektif menurunkan nilai distress pada pasien tumor otak di bandingkan dengan kelompok kontrol yang mendapatkan sesuai standar RS. Peningkatan pemberian terapi komplamenter seperti aromaterapi dapat menurunkan distress pada pasien tumor otak.

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Tumor are a lump that can form in any part of the body, caused by abnormal and

uncontrolled cell growth in and around tissue.Brain tumor patients will feel emotions

such as shock, agitation, anger, sadness, and withdrawal. Withdrawal arises due to fear

of possible consequences that occur, such as body image changes or death resulting in

distress in brain tumor patients. DistressIt is a multifactorial unpleasant experience that stems from a variety of factors, including psychological aspects (such as cognitive, behavioral, and emotional), social, spiritual, and physical aspects that can interfere with a person's ability to effectively manage cancer, its physical symptoms and treatment. One way to reduce distress is with aromatherapy. This study aims to determine the effectiveness of aromatherapy on distress values in brain tumor patients. This study uses a quasi-experimental research design with a non-randomized pre-test-post-test with control design. The sample size in this study used random sampling which amounted to 15 participants in the intervention group and 15 respondents in the control group. Data

analysis obtained a value of p value = 0.000 (p<0.05) which means that there is a difference in

significant distress values in the intervention group and control group. The

administration of aromatherapy was proven to be effective in reducing the distress value

in brain tumor patients compared to the control group that received according to hospital

standards. Increased delivery of complementary therapiessuch as aromatherapy can

reduce distress in brain tumor patients"

"Kanker prostat merupakan salah satu penyakit yang menjadi penyebab kematian utama di kalangan pria. Deteksi dini melalui pemindaian medis dapat membantu dalam pengobatan dan penanganan yang efektif. Namun, interpretasi dari pemindaian ini seringkali sulit dan memerlukan keahlian klinis yang tinggi oleh para ahli patologi. Selain itu keterbatasan dataset publik dengan bentuk biopsi H&E dengan anotasi level biopsy hinggal level patch yang tersedia terbatas jumlahnya sehingga menyebabkan pelatihan machine learning menjadi lebih sulit. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan dataset dengan model machine learning yang dapat membantu mengimprove model machine learning pengklasifikasi kanker prostat. Model machine learning yang digunakan untuk mengembangkan dataset dalam penelitian ini adalah conditional Progressive Growing GAN (ProGleason-GAN), sebuah jenis jaringan saraf tiruan yang dapat digunakan untuk mempelajari dan menghasilkan gambar sintetis dari pemindaian prostat yang telah menunjukkan hasil yang menjanjikan dalam generasi gambar sintetis beresolusi tinggi. Dataset yang ditambahkan dengan hasil gambar sintesis ProGleason-GAN digunakan untuk melatih model klasifikasi kanker prostat yaitu Semi Supervised Learning yang di gabungkan dengan Multiple Instance Learning. Dataset yang yang berisikan dataset SICAPv2 yang ditambahkan dengan hasil augmentasi ProGleason-GAN dinamakan SICAPv2 augmented. Penulis juga mengembangkan model klasifikasi dengan penambahan batch normalization yang dimana memungkinkan setiap batch data yang diberikan ke jaringan untuk dinormalisasi terlebih dahulu sebelum diolah lebih lanjut oleh jaringan. Ketika model klasifikasi ditambahkan dengan batch normalization serta dilatih dengan SICAPv2 augmented , maka nilai accuracy yang dihasilkan sebesar 76% lebih tinggi 4% model acuan.

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Prostate cancer is a disease that is the main cause of death among men. Early detection through medical scanning can help in effective treatment and management. However, interpretation of these scans is often difficult and requires a high degree of clinical skill by pathologists. In addition, the limited number of available public datasets in the form of H&E biopsies with biopsy level to patch level annotations makes machine learning training more difficult. Therefore, this research aims to develop a dataset with a machine learning model that can help improve machine learning models for prostate cancer classification. The machine learning model used to develop the dataset in this research is Conditional Progressive Growing GAN (ProGleason-GAN), a type of artificial neural network that can be used to learn and generate synthetic images from prostate scans which has shown promising results in the generation of high-resolution synthetic images. tall. The dataset added with the ProGleason-GAN synthetic image results is used to train a prostate cancer classification model, namely Semi Supervised Learning combined with Multiple Instance Learning. The dataset containing the SICAPv2 dataset added with the results of ProGleason-GAN augmentation is called SICAPv2 augmented. The author also developed a classification model with the addition of batch normalization, which allows each batch of data given to the network to be normalized first before being further processed by the network. When the classification model was added with batch normalization and trained with augmented SICAPv2, the resulting accuracy value was 76%, 4% higher than the reference model."

"Tumor otak sekunder dapat menyebabkan masalah nutrisi. Manifestasi klinis penurunan selera makan, gangguan menelan, mual, muntah, hemiparesis, kejang, gangguan fungsional dan kognitif dapat menurunkan asupan makanan dan berat badan sehingga berisiko malnutrisi. Perubahan metabolisme makronutrien dan mikronutrien yang terjadi juga memengaruhi terjadinya malnutrisi. Tatalaksana terapi medik gizi yang diberikan bertujuan mempertahankan atau memperbaiki status gizi sehingga meningkatkan kualitas hidup dan memperlama harapan hidupnya. Terapi medik gizi yang sesuai rekomendasi European Society of Clinical Nutrition and Metabolism (ESPEN) adalah diet seimbang yang meliputi makronutrien, mikronutrien, nutrien spesifik, dan edukasi. Pasien serial kasus ini adalah perempuan, berusia antara 48 sampai 59 tahun dengan diagnosis tumor otak sekunder. Tiga pasien memiliki tumor primer kanker payudara, sedangkan satu pasien dengan kanker endometrium. Skrining menggunakan malnutrition screening tool (MST) dilanjutkan asesmen gizi. Terapi medik gizi diberikan sesuai rekomendasi ESPEN dan toleransi pasien. Pemantauan gizi meliputi pemeriksaan fisik, antropometri, komposisi tubuh, kapasitas fungsional dan analisis asupan. Hasil menunjukkan semua pasien mencapai asupan makan sesuai target pemberian makronutrien, mikronutrien, dan nutrien spesifik. Status gizi berhasil dipertahankan dengan tiga pasien mengalami peningkatan BB. Kapasitas fungsional keempat pasien menunjukkan perbaikan dengan menggunakan Karnofski dan Eastern Cooperative Oncology Group (ECOG). Pemeriksaan handgrip hanya dapat dilakukan pada 3 pasien menunjukkan perbaikan.

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A secondary brain tumor may cause nutritional problems. Clinical manifestations such as decreased appetite, swallowing disorders, nausea, vomiting, hemiparesis, seizures, functional and cognitive disorders may reduce food intake and increase malnutrition. Also changes in metabolism can affect the malnutrition. The aim of the medical nutrition therapy to maintain nutritional status to improve the quality of life and life expectancy. Balance diet were recommended by European Society of Clinical Nutrition and Metabolism (ESPEN) includes macronutrients, micronutrients, specific nutrients with continuing nutrition education. Patients are females, aged 48 to 59 years, with secondary brain tumor. The primary tumor of three patients were breast cancer and one patient was endometrial cancer. Screening was done using the malnutrition screening tool (MST) and followed with nutritional assessment. Medical nutrition therapy were given based on ESPEN recommendations and patient tolerance. Nutrition monitoring includes physical examination, anthropometry, body composition, functional capacity and intake analysis. Patient’s monitoring showed that all patients achieved their intake targets. The body weight of three patient increased showed that the nutrition status was maintained well enough. Patient’s functional capacity were improved according to Karnofsky and Eastern Cooperative Oncology Group (ECOG). Handgrip examination were also improve when it was assesed on three patients."

"[ABSTRAKTumor otak (TO) merupakan penyebab kematian kedua darisemua kanker yang terjadi pada anak. TO memiliki gambaran klinis, radiologisdan histopatologis yang sangat bervariasi karena proses pengembangan sel-seljaringan otak masih berlanjut sampai usia 3 tahun. Data penelitian mengenai TOpada anak masih sedikit.Tujuan: Untuk mengetahui gambaran klinis, radiologis, histopatologis dan faktorprognostik TO di Departemen Ilmu Kesehatan Anak FKUI/ RS. Dr.Ciptomangunkusumo Jakarta periode tahun 2010 - 2015.Metode Penelitian: Kohort retrospektif dilakukan pada semua anak dengan TOprimer yang berobat/dirawat di Departemen Ilmu Kesehahatan Anak FKUI/RSDr. Ciptomangunkusumo Jakarta.Hasil: Didapatkan 88 pasien TO primer, terdiri dari 16 pasien berusia kurang dari3 tahun dan 72 pasien berusia lebih dari 3 tahun, laki-laki 53% dan perempuan47%. Anak usia kurang dari 3 tahun mengalami gejala sakit kepala (63%) dankejang (56%), berdasarkan radiologis letak TO yang terbanyak adalah di cerebralventrikel (25%) dan cerebellum (24%), berdasarkan histopatologis jenis TO yangterbanyak adalah Astrositoma (31%) dan Medulloblastoma (25%). Anak usialebih dari 3 tahun mengalami gejala sakit kepala (81%) dan gangguan penglihatan(65%), berdasarkan radiologis letak TO yang terbanyak adalah di cerebellum(24%) dan suprasellar (10 %), berdasarkan histopatologis jenis TO yangterbanyak adalah Medulloblastoma (21%), Astrositoma (18%) dan Glioma (17%).Angka kehidupan TO adalah 37 %. Tidak didapatkan faktor prognostik TO yangbermakna.Kesimpulan: Gejala TO tersering adalah sakit kepala, berdasarkan radiologisletak tumor terbanyak adalah di cerebellum serta berdasarkan histopatologis jenistumor terbanyak adalah Medulloblastoma dan Astrositoma. Tidak didapatkanfaktor prognostik TO pada anak.

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ABSTRACTPrimary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.;Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor., Background: Primary brain tumors rank second as the most frequent neoplasm inchildren. The lesions occurring in neonates or infants have been reported to differfrom those in older children in terms of their clinical presentation, radiology andhistopathology features.Objective To clarify the clinical presentation, radiology, histopathology features.and prognostic factor of primary brain tumors in Child DepartmentCiptomangunkusumo Hospital Jakarta in 2010 - 2015.Method: Retrospective cohort using medical records and neuroradiological danhistopathological studies, we analyzed each patient?s clinical presentation, tumorlocation, histopathological diagnosis and treatment then we compared betweenunder 3 years of age and more 3 years of age . The patients were followed untiltheir death or until the end of October 2015.Result: 88 patient of primer brain tumor that consist of 16 patients with under 3years of age and 72 patients with more 3 years of age. Boys are 53% and girlsare 47% . The most symptoms of children under 3 years of age is headache (63%)and seizure (56%), based on radiology the most location tumor is cerebralventrikel (25%) and cerebellum (24%), based on histopathology the predominanttumor is Astrositoma (31%) and Medulloblastoma (25%). The most symptomsof children more 3 years of age is headache (81%) and visual difficulties (65%),based on radiology the most tumor location is cerebellum (24%) and suprasellar(10 %), based on histopathology the predominat tumor is Medulloblastoma(21%), Astrositoma (18%) and Glioma (17%). The life expectancy rate is 37 %.There is no prognostic factor of brain tumor.Conclusion: The most symptom of brain tumor is headache, based on radiologythe most tumor location is cerebellum, and based on histopathology thepredominant tumor is Medulloblastoma and Astrositoma. There is no prognosticfactor of brain tumor.]"

"Latar belakang: Gangguan fungsi kognitif merupakan salah satu defisit neurologis kedua tersering setelah sakit kepala pada tumor intrakranial. Gangguan fungsi kognitif yang paling sering terjadi pada tumor otak adalah gangguan fungsi eksekutif. Penilaian fungsi kognitif sebelum dilakukan operasi maupun radioterapi penting sebagai data dasar klinis pasien. Tujuan: Mendapatkan informasi mengenai penilaian fungsi kognitif sebelum dilakukan operasi maupun radioterapi sebagai data dasar klinis pasien. Metode: Disain penelitian ialah survei potong lintang dengan pengambilan sampel secara konsekutif. Data diperoleh dari Divisi Fungsi Luhur Poliklinik saraf dan Departemen Rekam Medis RSUPN Cipto Mangunkusumo periode Januari 2009-Maret 2016. Subjek penelitian berusia 18-65 tahun dan telah terdiagnosis tumor otak, memiliki hasil histopatologi, serta telah menjalani pemeriksaan fungsi luhur preoperatif. Hasil: Terdapat 77 subjek penelitian dengan proporsi subjek laki-laki (50,6%) dan perempuan (49,4%) hampir sama, terbanyak berusia 40 tahun ke atas (67,5%), serta berpendidikan terutama 12 tahun ke atas (61%). Glioma (46,7%) dan meningioma (63,2%) merupakan dua tumor otak primer terbanyak, sedangkan paru (34,4%) dan payudara (18,8%) adalah asal metastasis otak terbanyak. Hampir semua subjek mengalami gangguan fungsi kognitif (96,1%), terutama ranah jamak (93,2%). Ranah memori dan fungsi eksekutif merupakan dua ranah yang paling sering terganggu. Proporsinya semua metastasis dan 80% tumor otak primer mengalami gangguan memori. Sebesar 77,5% tumor primer dan 89,7% metastasis otak mengalami gangguan fungsi eksekutif. Kesimpulan: Hampir semua fungsi kognitif pada tumor otak primer dan metastasis terganggu, tetapi gangguan pada metastasis otak lebih berat. Ranah jamak merupakan ranah yang paling banyak terganggu, terutama memori dan fungsi eksekutif.

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Aim: To obtain information about cognitive assessment before surgery and radiotherapy.

Methods: This study was a cross-sectional retrospective study using consecutive sampling. Data obtained from neurobehavior division of Neurology Clinic and Medical Record Department of Cipto Mangunkusumo Hospital started at January 2009 to April 2016. Subjects, aged 18 to 65 years old, diagnosed brain tumors, had histopatologic data, and done cognitive exam before surgery.

Results: There were 77 subjects, with no notable difference in gender proportion (50,6% male subjects and 49,4% female subjects). All were aged 40 years old above (67,5%) and had education level not lower than 12 years (61%). Glioma (46,7%) and meningioma (63,2%) are two most common primary brain tumors, whilst lungs (34,4%) and breast (18,8%) are two most major brain metastasis origin. Most subjects had cognitive impairments (96,1%), predominantly multidomain (93,2%). Of all domain, memory and executive function are mostly affected. All metastasis, and 80% primary brain tumor had memory impairment and 77,5% primary brain tumor and 89,7% brain metastasis had executive impairment.

Conclusion: Almost all cognitive domain impaired in brain tumors, particularly in brain metastasis. It suggested that multiple cognitive domain impairment were majorly impaired, with memory and executive function as the most common domain."

"Diabetes melitus dikaitkan dengan peningkatan risiko kejadian berbagai jenis kanker pada banyak studi. Namun demikian, hubungan nya dengan risiko tumor otak masih kontroversial. Beberapa studi menunjukkan adanya korelasi positif, negatif, atau bahkan tidak sama sekali antara keduanya. Tumor otak tidak menyumbang pada sebagian besar kasus kanker, tetapi memiliki tingkat mortalitas yang tinggi dengan rata-rata kelangsungan hidup yang rendah, sementara terapi masih sangat terbatas. Penulisan review ini bertujuan untuk menilai hubungan antara diabetes melitus dengan risiko tumor otak dan kaitannya dengan kelangsungan hidup pasien, serta melihat potensi terapi antidiabetes terhadap tumor otak. Review bersifat sistematik berdasarkan acuan Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) tahun 2009 dan menggunakan pendekatan kualitatif. Pencarian literatur dilakukan pada Oxford Journals, ProQuest, PubMed, ScienceDirect, Scopus, SpringerLink, dan Wiley, serta melalui daftar referensi pada artikel terkait. Hasil pencarian didapatkan delapan artikel yang sesuai dengan kriteria yang ditetapkan. Berdasarkan analisis pada artikel tersebut, perbedaan hubungan antara diabetes melitus dengan tumor otak dapat terjadi akibat sub kelompok yang berbeda, yaitu jenis kelamin, ras, serta jenis studi. Tingginya nilai HbA1c dapat dijadikan prediktor bagi kelangsungan hidup yang lebih rendah. Meskipun hasil ini tidak bersifat independen, kontrol glikemik merupakan salah satu faktor yang perlu diperhatikan pada pasien tumor otak. Terkait hubungannya dengan terapi antidiabetes, metformin menunjukkan adanya potensi sebagai terapi adjuvan bagi pasien tumor otak dikarenakan meningkatkan kelangsungan hidup yang lebih lama pada pasien glioma stadium III dibandingkan dengan insulin dan sulfonilurea, adanya potensi efek antiproliferatif pada sel glioma, dan tidak menyebabkan hipoglikemia."