A nomogram to predict vascular invasion before resection of colorectal cancer

Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography

BACKGROUND

Risk evaluation of lymph node metastasis (LNM) in superficial colorectal cancer resected by endoscopic surgery is critical for determining subsequent therapeutic strategies, but the role of existing clinical methods, including computed tomography, remains limited.

METHODS

Features of the nomogram were determined by logistic regression analysis, and the performance was validated by calibration plots, ROC curves and DCA curves in both the training set and the validation set.

RESULTS

A total of 608 consecutive superficial CRC cases were randomly divided into 426 training and 182 validation cases. Univariate and multivariate logistic regression analyses revealed that age < 50, tumour budding, lymphatic invasion and lower HDL levels were risk factors for LNM. Stepwise regression and the Hosmer‒Lemeshow goodness of fit test showed that the nomogram had good performance and discrimination, which was validated by ROC curves and calibration plots. Internal and external validation demonstrated that the nomogram had a higher C-index (training group, 0.749, validation group, 0.693). DCA and clinical impact curves graphically show that the use of the nomogram to predict LNM had remarkable predictive power. Finally, in comparison with CT diagnosis, the nomogram also visually showed higher superiority, as demonstrated by ROC, DCA and clinical impact curves.

CONCLUSION

Using common clinicopathologic factors, a noninvasive nomogram for individualized prediction of LNM after endoscopic surgery was conveniently established. Nomograms have great superiority in the risk stratification of LNM compared with traditional CT imaging.

A Nomogram to Predict Critical Weight Loss in Patients with Nasopharyngeal Carcinoma During (Chemo) Radiotherapy

Background:

Weight loss is an important side effect of long-term anticancer treatment for nasopharyngeal carcinoma patients. The decline in body function will cause many adverse effects, such as local recurrence and distant metastasis, and reduce the patient’s quality of life. Therefore, this study developed a predictive model for the probability of critical weight loss to provide timely appropriate nutritional interventions and prevent serious side effects.

Methods:

A 20-week prospective follow-up study of 137 nasopharyngeal carcinoma patients in West China Hospital of Sichuan University undergoing radiotherapy and chemotherapy from February 2018 to March 2020 was conducted to collect relevant clinical data. The clinical usefulness and calibration of the prediction model were assessed using the C-index, calibration plot, receiver operating curve, and decision curve analysis. Internal validation was assessed using bootstrapping validation.

Results:

The nomogram consisted of sex, smoking status, physical status, chemotherapy regimen, and body mass index. Good calibration was observed for the cohort, with an area under the curve of 0.924. Five independent prognostic factors were included in the nomogram, which showed a high C-index value of 0.815 in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when the intervention was decided at the critical weight loss possibility threshold in the 0% to 97% range.

Conclusions:

We constructed and validated a nomogram for predicting the incidence of critical weight loss in nasopharyngeal cancer patients undergoing chemotherapy and radiotherapy.

A Novel Nomogram for Individually Predicting of Vascular Invasion in Gastric Cancer

PURPOSE

Vascular invasion (VI) is associated with recurrence and is an indicator of poor prognosis in gastric cancer (GC). Pre-operative identification of VI may guide the selection of the optimal surgical approach and assess the requirement for neoadjuvant therapy.

METHODS

A total of 271 patients were retrospectively collected and randomly allocated into the training and validation datasets. The least absolute shrinkage and selection operator regression model was used to select potentially relevant features, and multivariable logistic regression analysis was used to develop the nomogram.

RESULTS

The nomogram consisted of pre-operative serum complement C3 levels, duration of symptoms, pre-operative computed tomography stage, abdominal distension and undifferentiated carcinoma. The nomogram provided good calibration for both the training and the validation set, with area under the curve values of 0.792 and 0.774. Decision curve analysis revealed that the nomogram was clinically useful.

CONCLUSION

The present study constructed a nomogram for the pre-operative prediction of VI in patients with GC. The nomogram may aid the identification of high-risk patients and aid the optimization of pre-operative decision-making.

Nomogram based on clinical characteristics for preoperative prediction of perineural invasion in gastric cancer

Purpose

Gastric cancer (GC) has a poor prognosis and high rate of recurrence. Perineural invasion (PNI) is a prognostic factor in GC that is associated with a high risk of systemic recurrence. Preoperative identification of PNI may facilitate patient stratification and optimal preoperative treatment. We therefore developed and validated a nomogram for the preoperative prediction of PNI.

Methods

We retrospectively collected clinical data from 261 GC patients, who were randomly assigned to training (n = 185) and validation (n = 76) sets. The least absolute shrinkage and selection operator regression model was used to identify potentially relevant clinical parameters, and multivariable logistic regression analysis was used to develop the nomogram.

Results

The nomogram consisted of body mass index, immunoglobulin A level, and computed tomography-based T- and N-stages. Good calibration was observed for both the training and validation sets, with areas under the curve of 0.77 and 0.79, respectively. Decision curve analysis revealed that the nomogram was clinically relevant.

Conclusion

We developed and validated a nomogram for the preoperative prediction of PNI in patients with GC. Our nomogram may facilitate the identification of high-risk patients and optimization of preoperative decision-making.

Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor representing more than 90% of primary liver cancer. This study aimed to identify metabolism-related biomarkers with prognostic value by developing the novel prognostic score (PS) model. Transcriptomic profiles derived from TCGA and EBIArray databases were analyzed to identify differentially expressed genes (DEGs) in HCC tumor samples compared with normal samples. The overlapped genes between DEGs and metabolism-related genes (crucial genes) were screened and functionally analyzed. A novel PS model was constructed to identify optimal signature genes. Cox regression analysis was performed to identify independent clinical factors related to prognosis. Nomogram model was constructed to estimate the predictability of clinical factors. Finally, protein expression of crucial genes was explored in different cancer tissues and cell types from the Human Protein Atlas (HPA). We screened a total of 305 overlapped genes (differentially expressed metabolism-related genes). These genes were mainly involved in "oxidation reduction," "steroid hormone biosynthesis," "fatty acid metabolic process," and "linoleic acid metabolism." Furthermore, we screened ten optimal DEGs (CYP2C9, CYP3A4, and TKT, among others) by using the PS model. Two clinical factors of pathologic stage (P < .001, HR: 1.512 [1.219-1.875]) and PS status (P <.001, HR: 2.259 [1.522-3.354]) were independent prognostic predictors by cox regression analysis. Nomogram model showed a high predicted probability of overall survival time, and the AUC value was 0.837. The expression status of 7 proteins was frequently altered in normal or differential tumor tissues, such as liver cancer and stomach cancer samples.We have identified several metabolism-related biomarkers for prognosis prediction of HCC based on the PS model. Two clinical factors were independent prognostic predictors of pathologic stage and PS status (high/low risk). The prognosis prediction model described in this study is a useful and stable method for novel biomarker identification.