Latar Belakang: Osteosarkoma merupakan keganasan tulang primer dengan beragamsubtipe dan memerlukan pendekatan multidisiplin dalam diagnosis dan tatalaksananya.Hingga saat ini belum ada alat diagnostik yang terbukti dapat mendekaticlinicopathological conference (CPC) sebagai standar baku emas. Keterbatasan fasilitas,biaya, dan antrian pemeriksaan yang panjang sering kali menunda diagnosisosteosarkoma. Penelitian ini bertujuan untuk membuat model sistem skoring berdasarkantemuan klinis, laboratorium, radiografi konvensional, dan histopatologis untukmendiagnosis osteosarkoma secara cepat dan tepat.Metode: Penelitian ini dilakukan dalam dua tahap. Tahap pertama bertujuan untukmemformulasikan sistem skoring untuk mendiagnosis osteosarkoma menggunakan datasekunder secara retrospektif di RS. Dr. Cipto Mangunkusumo tahun 2016 hingga 2020.Studi ini melibatkan semua pasien dengan suspek keganasan tulang primer dandidiagnosis akhir berdasarkan CPC. Uji analisis dilakukan secara univariat, bivariat, danmultivariat menggunakan regresi logistik backward stepwise dilanjutkan dengan ujikalibrasi dan diskriminasi menggunakan uji Hosmer-Lemeshow dan kurva receivingoperator characteristic (ROC), serta menentukan titik potong pada model. Tahap keduaditujukan untuk mengevaluasi model sistem skoring yang diformulasi pada tahap pertamasecara prospektif menggunakan data primer sejak September 2022 hingga Desember2022 di poliklinik Orthopaedi dan Traumatologi RS. Dr. Cipto Mangunkusumo.Hasil: Penelitian tahap pertama melibatkan 120 subjek dan menghasilkan dua modelsistem skoring, yaitu dengan mempertimbangkan riwayat pijat (model 1) dan tanpamempertimbangkan riwayat pijat (model 2). Dari hasil analisis multivariat, didapatkansembilan variabel yang dimasukan dalam model sistem skoring yaitu usia, indeks massatubuh (IMT), onset, riwayat pijat, lokasi tumor, kadar alkaline phosphatase (ALP), laktatdehidrogenase (LDH), letak lesi berdasarkan radiografi konvensional, serta gambaranhistopatologis berdasarkan fine needle aspiration biopsy (FNAB). Uji kalibrasi model 1dan 2 menunjukan kalibrasi yang baik (p=0,498 dan p=0,917). Uji diskriminasi padamodel sistem skoring menunjukan nilai area under the curve (AUC) 0,818 dengan nilaip<0,001 pada model 1 dan 2. Titik potong pada model 1 dan 2 berturut-turut adalah 19dan 11 poin. Penelitian tahap kedua melibatkan 34 subjek dan menunjukan sensitivitas,spesifisitas, dan akurasi model 1 dan 2 berturut turut sebesar 81,25% dan 87,5%, 100%dan 100%, dan 91,1% dan 94,1%.Kesimpulan: Didapatkan dua model sistem skoring yang mampu mendiagnosisosteosarkoma dengan cepat dan tepat dibandingkan dengan CPC. Lokasi tumor di lututdan gambaran sel pleiomorfik dengan atau tanpa matriks osteoid ganas merupakan faktoryang paling berpengaruh terhadap diagnosis osteosarkoma.

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Introduction : Osteosarcoma, being one of the most prevalent among the primary bone

malignancies, consists of multiple subtypes and requires a multidisciplinary approach for

proper diagnosis and treatment. Lately, there have not been a diagnostic tool that is able

to rival the accuracy of clinicopathological conference (CPC) as a gold standard in

determining the diagnosis and treatment of osteosarcoma. Limitations in budgeting, as

well as the time taken for each patient to undergo supporting examinations often leads to

a delayed diagnosis. This research aims to create a scoring system that is based on clinical

symptoms, laboratory results, conventional radiology, as well as histopathological results

to establish a quick and accurate diagnosis for osteosarcoma.

Method: This research was conducted in two stages; the first stage aims to formulate the

scoring system for diagnosing osteosarcoma by using a retrospective, secondary data

obtained from Dr. Cipto Mangunkusumo Hospital from 2016 up to 2020. This study

involved all patients with suspected bone malignancies that was eventually diagnosed

with osteosarcoma by means of CPC. The analysis was done with univariate, bivariate,

and multivariate analysis using backward stepwise logistic regression method followed

by calibration and discrimination test using Hosmer-Lemeshow test and receiving

operator characteristic (ROC) curve analysis, and determined the cut-off point in the

scoring system model. The second stage was aimed to prospectively evaluate the

previously formulated scoring system model in the first stage using primary data from

September 2022 to December 2022 at Orthopaedic and Traumatology outpatient clinic

Dr. Cipto Mangunkusumo Hospital.

Result: The first stage of the study involved 120 subjects and resulted two models of

scoring system, namely by considering massage history (model 1) and without

considering massage history (model 2). From multivariate analysis, nine variables were

included in the scoring system model, including age, body mass index (BMI), onset,

massage history, tumor location, alkaline phosphatase (ALP) levels, lactate

dehydrogenase (LDH), location of the lesion based on conventional radiography, and

histopathological finding based on fine needle aspiration biopsy (FNAB). Calibration

tests for models 1 and 2 showed good calibration (p=0.498 and p=0.917). The

discrimination test on the scoring system model showed an area under the curve (AUC)

value of 0.818 with a p-value <0.001 in both models 1 and 2. The cut-off points in model

1 and 2 were 19 and 11, respectively. The second stage of the study involved 34 subjects

with the sensitivity, specificity, and accuracy of models 1 and 2 showing 81.25% and

87.5%, 100% and 100%, and 91.1% and 94.1%, respectively.

Conclusion: This study has proposed two models of scoring systems that can be used

for a more rapid and accurate diagnosis of osteosarcoma when compared to CPC; the

location of the tumor mass in the knee joint and the appearance of pleomorphic cells, with

or without the appearance of malignant osteoids, both being significant factors in

diagnosing osteosarcoma