Development and Validation of a Nomogram for Preoperative Prediction of Perineural Invasion in Colorectal Cancer

Abstract

BackgroundIn colorectal cancer (CRC), perineural invasion (PNI) is usually identified histologically in biopsy or resection specimens and is considered a high-risk feature for recurrence of CRC and is an indicator for adjuvant therapy. Preoperative identification of PNI could help determine the need for adjuvant therapy and the approach to surgical resection. This study aimed to develop and validate a nomogram for the preoperative prediction of PNI in patients with CRC.Material/MethodsA total of 664 patients with CRC from a single center were classified into a training dataset (n=468) and a validation dataset (n=196). The least absolute shrinkage and selection operator (LASSO) regression model was used to select potentially relevant features. Multivariate logistic regression analysis was used to develop the nomogram. The performance of the nomogram was assessed based on its calibration, discrimination, and clinical utility.ResultsThe nomogram consisted of five clinical features and provided good calibration and discrimination in the training dataset, with an area under the curve (AUC) of 0.704 (95% CI, 0.657–0.751). Application of the nomogram in the validation cohort showed acceptable discrimination, with the AUC of 0.692 (95% CI, 0.617–0.766) and good calibration. Decision curve analysis (DCA) showed that the nomogram was clinically useful.ConclusionsThe nomogram developed in this study might allow clinicians to predict the risk of PNI in patients with CRC preoperatively. The nomogram showed favorable discrimination and calibration values, which may help optimize preoperative treatment decision-making for patients with CRC.

Personalizing prognostic prediction in early-onset Colorectal Cancer

“…Perineural invasion can occur independently when there is no blood or lymph invasion, and sometimes may be the sole metastasis way for CRC [36]. Perineural invasion is a clinical sign of cancer metastasis and thus signifies the unfavorable prognosis of patients with CRC [37,38]. However, this clinical factor with great prognostic significance has never been explored in patients with EOCRC.…”

Development and Validation of a Novel Model for Predicting the 5-Year Risk of Type 2 Diabetes in Patients with Hypertension: A Retrospective Cohort Study

“…Although it has been reported that more than 40 T2D risk prediction models have been established among different populations, there are few risk prediction models based on the East Asian race, especially the Chinese population [ 27 – 30 ]. Taking into account the genetic and environmental differences (i.e., economic level, diet, climate, and lifestyle), the intensity or distribution of T2D risk factors varies among different populations, complex mathematical formulas, and the lack of simple and intuitive tools to facilitate the use of these predictive risk models [ 14 ]. Therefore, few models are currently used clinical practice.…”

“…It estimates the regression coefficient by maximizing the logarithmic likelihood function and limits the sum of the absolute values of the regression coefficients [ 13 ]. The regression coefficient estimated by LASSO is very sparse, and many components are exactly 0 [ 14 ]. Therefore, the LASSO automatically removes unnecessary covariates.…”

Nomogram for predicting overall survival in stage II‐III colorectal cancer

“…In addition, sophisticated and expensive laboratory techniques have limits to their application. Nomograms have been widely used in clinical oncology as reliable tools for estimating numerical probabilities for individual patients by incorporating and illustrating important prognostic factors . This has been demonstrated in several types of cancers, including breast, lung, and gastric cancers, and the predictions of these nomograms may be more accurate than those based on the traditional TNM staging systems for various tumor types .…”