Mohammadhadi Khorrami1, Prantesh Jain2, Kaustav Bera1, Mehdi Alilou1, Rajat Thawani3, Pradnya Patil4, Usman Ahmad5, Sudish Murthy5, Kevin Stephans6, Pinfu Fu7, Vamsidhar Velcheti8, Anant Madabhushi9. 1. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. 2. Department of Hematology/Oncology, University Hospitals Seidman Cancer Center, Case Comprehensive Cancer Center, Cleveland, OH, USA. 3. Maimonides Medical Center, 4802 10th Ave, Brooklyn, NY 11219, USA. 4. Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, OH, USA. 5. Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, OH, USA. 6. Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA. 7. Department of Population and Quantitative Health Sciences, CWRU, Cleveland, OH, USA. 8. Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA. 9. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, OH, USA. Electronic address: anant.madabhushi@case.edu.
Abstract
OBJECTIVE: The use of a neoadjuvant chemoradiation followed by surgery in patients with stage IIIA NSCLC is controversial and the benefit of surgery is limited. There are currently no clinically validated biomarkers to select patients for such an approach. In this study we evaluate computed tomography (CT) derived intratumoral and peritumoral texture and nodule shape features in their ability to predict major pathological response (MPR). MPR being defined as ≤10% of residual viable tumor, assessed at the time of surgery. MATERIAL AND METHODS: Ninety patients with stage III NSCLC treated with chemoradiation prior to surgical resection were selected. The patients were divided randomly into two equal sets, one for training and one for independent testing. The radiomic texture and shape features were extracted from within the nodule (intra) and from the parenchymal regions immediately surrounding the nodule (peritumoral). A univariate regression analysis was performed on the image and clinicopathologic variables and then included into a multivariable logistic regression (MLR) for binary outcome prediction of MPR. The radiomic signature risk-score was generated by using a multivariate Cox regression model and association of the signature with OS and DFS was also evaluated. RESULTS: Thirteen stable and predictive intratumoral and peritumoral radiomic texture features were found to be predictive of MPR. The MLR classifier yielded an AUC of 0.90 ± 0.025 within the training set and a corresponding AUC = 0.86 in prediction of MPR within the test set. The radiomic signature was also significantly associated with OS (HR = 11.18, 95% CI = 3.17, 44.1; p-value = 0.008) and DFS (HR = 2.78, 95% CI = 1.11, 4.12; p-value = 0.0042) in the testing set. CONCLUSION: Texture features extracted within and around the lung tumor on CT images appears to be associated with the likelihood of MPR, OS and DFS to chemoradiation.
OBJECTIVE: The use of a neoadjuvant chemoradiation followed by surgery in patients with stage IIIA NSCLC is controversial and the benefit of surgery is limited. There are currently no clinically validated biomarkers to select patients for such an approach. In this study we evaluate computed tomography (CT) derived intratumoral and peritumoral texture and nodule shape features in their ability to predict major pathological response (MPR). MPR being defined as ≤10% of residual viable tumor, assessed at the time of surgery. MATERIAL AND METHODS: Ninety patients with stage III NSCLC treated with chemoradiation prior to surgical resection were selected. The patients were divided randomly into two equal sets, one for training and one for independent testing. The radiomic texture and shape features were extracted from within the nodule (intra) and from the parenchymal regions immediately surrounding the nodule (peritumoral). A univariate regression analysis was performed on the image and clinicopathologic variables and then included into a multivariable logistic regression (MLR) for binary outcome prediction of MPR. The radiomic signature risk-score was generated by using a multivariate Cox regression model and association of the signature with OS and DFS was also evaluated. RESULTS: Thirteen stable and predictive intratumoral and peritumoral radiomic texture features were found to be predictive of MPR. The MLR classifier yielded an AUC of 0.90 ± 0.025 within the training set and a corresponding AUC = 0.86 in prediction of MPR within the test set. The radiomic signature was also significantly associated with OS (HR = 11.18, 95% CI = 3.17, 44.1; p-value = 0.008) and DFS (HR = 2.78, 95% CI = 1.11, 4.12; p-value = 0.0042) in the testing set. CONCLUSION: Texture features extracted within and around the lung tumor on CT images appears to be associated with the likelihood of MPR, OS and DFS to chemoradiation.
Authors: Mohammadhadi Khorrami; Kaustav Bera; Patrick Leo; Pranjal Vaidya; Pradnya Patil; Rajat Thawani; Priya Velu; Prabhakar Rajiah; Mehdi Alilou; Humberto Choi; Michael D Feldman; Robert C Gilkeson; Philip Linden; Pingfu Fu; Harvey Pass; Vamsidhar Velcheti; Anant Madabhushi Journal: Lung Cancer Date: 2020-02-26 Impact factor: 5.705
Authors: Hannah Able; Amber Wolf-Ringwall; Aaron Rendahl; Christopher P Ober; Davis M Seelig; Chris T Wilke; Jessica Lawrence Journal: PLoS One Date: 2021-08-17 Impact factor: 3.240
Authors: Mohammadhadi Khorrami; Kaustav Bera; Rajat Thawani; Prabhakar Rajiah; Amit Gupta; Pingfu Fu; Philip Linden; Nathan Pennell; Frank Jacono; Robert C Gilkeson; Vamsidhar Velcheti; Anant Madabhushi Journal: Eur J Cancer Date: 2021-03-17 Impact factor: 9.162
Authors: Harini Veeraraghavan; Claire F Friedman; Deborah F DeLair; Josip Ninčević; Yuki Himoto; Silvio G Bruni; Giovanni Cappello; Iva Petkovska; Stephanie Nougaret; Ines Nikolovski; Ahmet Zehir; Nadeem R Abu-Rustum; Carol Aghajanian; Dmitriy Zamarin; Karen A Cadoo; Luis A Diaz; Mario M Leitao; Vicky Makker; Robert A Soslow; Jennifer J Mueller; Britta Weigelt; Yulia Lakhman Journal: Sci Rep Date: 2020-10-20 Impact factor: 4.379