Abstract
PurposeTo investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer.MethodsA total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram.ResultsThe Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835–0.930) in the training dataset and 0.846 (95% CI 0.757–0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730–0.987).ConclusionA radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.
Highlights
Colorectal cancer (CRC) is the most common tumor in the digestive system, and its mortality rate ranks third among cancer-related mortality in the world [1]
A radiomics nomogram grounded on the multivariate logistic analysis was built for the training dataset
The predictive ability of the nomogram was quantified through the area under the curve (AUC) of a receiver operator characteristic (ROC) curve
Summary
Colorectal cancer (CRC) is the most common tumor in the digestive system, and its mortality rate ranks third among cancer-related mortality in the world [1]. Rectal cancer accounts for about one-third of all CRC cases [2]. The primary treatments for rectal cancer include chemotherapy, radiotherapy, and surgery, though treatment options are determined by tumor stage. Early-stage rectal cancer can be treated directly by surgery, whereas locally advanced rectal cancer (LARC) requires neoadjuvant radiochemotherapy before surgery. Accurate preoperative staging of rectal cancer is essential for achieving precision treatment. It is vital to be able to precisely identify preoperative staging
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