Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

• Published in: Cancers 2023-08, Vol.15 (15), p.3945
• Main Authors: Tanaka, Max D, Geubels, Barbara M, Grotenhuis, Brechtje A, Marijnen, Corrie A M, Peters, Femke P, van der Mierden, Stevie, Maas, Monique, Couwenberg, Alice M
• Format: Article
• Language: English
• Subjects: Adjuvant treatment; Analysis; Cancer; Cancer patients; Cancer therapies; Care and treatment; Chemoradiotherapy; Colorectal cancer; Content analysis; Deep learning; Evidence-based medicine; Metastasis; Multiple database searches; Neoadjuvant therapy; Oncology, Experimental; Pathology; Patients; Prediction models; Preservation; Quality of life; Radiation therapy; Radiomics; Rectum; Surgery; Systematic Review; Toxicity; Tumors; Validation studies
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15153945

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.