Accuracy of deep neural learning models in the imaging prediction of pathological complete response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer: a systematic review

Prabhakaran, S; Choong, KWK; Prabhakaran, S; Choy, KT; Kong, JCH

Author(s): Prabhakaran, S; Choong, KWK; Prabhakaran, S; Choy, KT; Kong, JCH;
Details: Publication Year 2023-08-18,Volume 408,Issue #1,Page 321
Journal Title: Langenbeck's Archives of Surgery
Publication Type: Review
Abstract: PURPOSE: Up to 15-27% of patients achieve pathologic complete response (pCR) following neoadjuvant chemoradiotherapy (CRT) for locally advanced rectal cancer (LARC). Deep neural learning (DL) algorithms have been suggested to be a useful adjunct to allow accurate prediction of pCR and to identify patients who could potentially avoid surgery. This systematic review aims to interrogate the accuracy of DL algorithms at predicting pCR. METHODS: Embase (PubMed, MEDLINE) databases and Google Scholar were searched to identify eligible English-language studies, with the search concluding in July 2022. Studies reporting on the accuracy of DL models in predicting pCR were selected for review and information pertaining to study characteristics and diagnostic measures was extracted from relevant studies. Risk of bias was evaluated using the Newcastle-Ottawa scale (NOS). RESULTS: Our search yielded 85 potential publications. Nineteen full texts were reviewed, and a total of 12 articles were included in this systematic review. There were six retrospective and six prospective cohort studies. The most common DL algorithm used was the Convolutional Neural Network (CNN). Performance comparison was carried out via single modality comparison. The median performance for each best-performing algorithm was an AUC of 0.845 (range 0.71-0.99) and Accuracy of 0.85 (0.83-0.98). CONCLUSIONS: There is a promising role for DL models in the prediction of pCR following neoadjuvant-CRT for LARC. Further studies are needed to provide a standardised comparison in order to allow for large-scale clinical application. PROPERO REGISTRATION: PROSPERO 2021 CRD42021269904 Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021269904 .
Publisher: Springer Nature
Keywords: Humans; Neoadjuvant Therapy; Prospective Studies; Retrospective Studies; Algorithms; *Neoplasms, Second Primary; *Rectal Neoplasms/diagnostic imaging/therapy; Artificial intelligence; Deep neural learning; Long course neoadjuvant chemoradiotherapy; Magnetic resonance imaging; Rectal cancer; Therapeutic response
Department(s): Surgical Oncology
PubMed ID: 37594552
Publisher's Version: https://doi.org/10.1007/s00423-023-03039-4
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2023-11-08 07:27:57

Last Modified: 2024-07-16 04:19:13