Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.
Joshua M K DavisMuhammad Khalid Khan NiaziAnsley B RickerThomas E TavolaraJordan N RobinsonBayram AnnanurovKaylee SmithRohit ManthaJimmy HwangRuchi ShresthaDavid A IannittiJohn B MartinieErin H BakerMetin N GurcanDionisios VrochidesPublished in: Journal of surgical oncology (2024)
Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.
Keyphrases
- neoadjuvant chemotherapy
- locally advanced
- deep learning
- rectal cancer
- liver metastases
- lymph node
- sentinel lymph node
- end stage renal disease
- cross sectional
- prognostic factors
- squamous cell carcinoma
- ejection fraction
- newly diagnosed
- radiation therapy
- chronic kidney disease
- peritoneal dialysis
- convolutional neural network
- machine learning
- artificial intelligence
- early stage
- patient reported outcomes
- fluorescence imaging
- photodynamic therapy