Development and validation of radiomic signature for predicting overall survival in advanced-stage cervical cancer.

Autor:	Jha, Ashish Kumar, Mithun, Sneha, Sherkhane, Umeshkumar B., Jaiswar, Vinay, Shah, Sneha, Purandare, Nilendu, Prabhash, Kumar, Maheshwari, Amita, Gupta, Sudeep, Wee, Leonard, Rangarajan, V., Dekker, Andre
Předmět:	RANDOM forest algorithms SQUAMOUS cell carcinoma CERVIX uteri tumors PREDICTION models DATA analysis RESEARCH funding RADIOMICS LOGISTIC regression analysis DIGITAL signatures CHEMORADIOTHERAPY POSITRON emission tomography computed tomography RETROSPECTIVE studies DESCRIPTIVE statistics RADIOISOTOPE brachytherapy SUPPORT vector machines STATISTICS DICOM (Computer network protocol) COMPARATIVE studies MACHINE learning DATA analysis software CONFIDENCE intervals OVERALL survival ALGORITHMS
Zdroj:	Frontiers in Nuclear Medicine; 2024, p1-13, 13p
Abstrakt:	Background: The role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance. Purpose: The purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features. Materials and Methods: Pretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation. Results: The average prediction accuracy was found to be 0.65 (95% CI: 0.60– 0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively. Conclusion: Our study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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