CT-based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study
Zhigang Yuan1, Marissa Frazer1, Geoffrey G Zhang1
1Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
Introduction:
Innovative biomarkers to predict treatment response in rectal cancer would be helpful in optimizing personalized treatment approaches. In this study, we aimed to develop and validate a CT-based radiomic imaging biomarker to predict pathological response.
Methods:
We used two independent cohorts of rectal cancer patients to develop and validate a CT-based radiomic imaging biomarker predictive of treatment response. A total of 91 rectal cancer cases treated from 2009 to 2018 were assessed for the tumour regression grade (TRG) (0 = pathological complete response, pCR; 1 = moderate response; 2 = partial response; 3 = poor response). Exploratory analysis was performed by combining pre-treatment non-contrast CT images and patterns of TRG. The models built from the training cohort were further assessed using the independent validation cohort.
Results:
The patterns of pathological response in training and validation groups were TRG 0 (n = 14, 23.3%; n = 6, 19.4%), 1 (n = 31, 51.7%; n = 15, 48.4%), 2 (n = 12, 20.0%; n = 7, 22.6%) and 3 (n = 3, 5.0%; n = 3, 9.7%), respectively. Separate predictive models were built and analysed from CT features for pathological response. For pathological response prediction, the model including 8 radiomic features by random forest method resulted in 83.9% accuracy in predicting TRG 0 vs TRG 1-3 in validation.
Conclusion:
The pre-treatment CT-based radiomic signatures were developed and validated in two independent cohorts. This imaging biomarker provided a promising way to predict pCR and select patients for non-operative management.
