CT-based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study

Autor:	Richard D. Kim, Seth Felder, Zhigang Yuan, Marissa Frazer, Kujtim Latifi, Julian Sanchez, Iman Imanirad, Geoffrey Zhang, Jessica M. Frakes, Eduardo G. Moros, Sarah E. Hoffe, Louis B. Harrison, Sophie Dessureault, Vladimir Feygelman
Rok vydání:	2020
Předmět:	Oncology Adult Male medicine.medical_specialty Tumour regression Imaging biomarker Colorectal cancer Pathological response 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests Internal medicine medicine Biomarkers Tumor Humans Radiology Nuclear Medicine and imaging Pathological Complete response Aged Neoplasm Staging Retrospective Studies Aged 80 and over business.industry Rectal Neoplasms Retrospective cohort study Exploratory analysis Chemoradiotherapy Middle Aged medicine.disease Neoadjuvant Therapy 030220 oncology & carcinogenesis Florida Female Neoplasm Grading business Tomography X-Ray Computed
Zdroj:	Journal of medical imaging and radiation oncologyReferences. 64(3)
ISSN:	1754-9485
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aba00cfd6c2fc83af164749bf2c0cc2f https://pubmed.ncbi.nlm.nih.gov/32386109 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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