AUTHOR=Jain Prantesh , Khorrami Mohammadhadi , Gupta Amit , Rajiah Prabhakar , Bera Kaustav , Viswanathan Vidya Sankar , Fu Pingfu , Dowlati Afshin , Madabhushi Anant 

TITLE=Novel Non-Invasive Radiomic Signature on CT Scans Predicts Response to Platinum-Based Chemotherapy and Is Prognostic of Overall Survival in Small Cell Lung Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.744724

DOI=10.3389/fonc.2021.744724

ISSN=2234-943X

ABSTRACT=Background: Small cell lung cancer (SCLC) is an aggressive malignancy characterized by initial chemosensitivity followed by resistance and rapid progression. There are no predictive biomarkers that can accurately guide the use of systemic therapy in SCLC patients. This study explores the role of radiomic features on pretreatment computed tomography (CT) scans to (a) prognosticate overall survival (OS) and (b) predict response to chemotherapy.
Methods: 153 SCLC patients were included and patients were divided into equally sized training (Str =77) and test sets (Ste =76). Textural descriptors were extracted from the nodule and regions surrounding the nodule (peritumoral). The endpoints of this study were overall survival (OS), progression free survival (PFS) and response to chemotherapy. The radiomic risk-score (RRS) was generated by using the least absolute shrinkage and selection operator (LASSO) with the Cox regression model. Patients were classified into the high-risk or low-risk groups based on the median of RRS. The features identified by LASSO were then used to train a linear discriminant analysis (LDA) classifier (MRad) to predict response to chemotherapy. A prognostic nomogram (NRad+Clin) was developed on Str by combining clinical and radiomic features and validated on Ste. To estimate the clinical utility of the nomogram, decision curve analysis (DCA) was performed.
Results: A univariable Cox regression analysis indicated that RRS was significantly associated with OS in Str (HR: 1.53; 95% confidence interval (CI), [1.1–2.2; P=0.021]; C-index=0.72) and Ste (HR: 1.4; [1.1–1.82]; P=0.0127; C-index=0.69). The RRS was significantly associated with PFS in Str [HR: 1.89; [1.4–4.61]; P = 0.047; C-index = 0.7] and Ste [HR: 1.641; [1.1–2.77]; P = 0.04; C-index = 0.67]. MRad was able to predict response to chemotherapy with an area under the receiver operating characteristic curve of 0.76 ± 0.03 within Str and 0.72 within Ste. The discrimination ability of the NRad+Clin model on Str and Ste were (C-index [95% confidence interval]: 0.68 [0.66–0.71] and 0.67 [0.63–0.69]), respectively. DCA indicated that the NRad+Clin model was clinically useful.
Conclusions: Radiomic features extracted within and around the lung tumor on CT images were both prognostic of OS and predictive of response to chemotherapy in SCLC patients.