An Examination of Lymph Node Sampling as a Predictor of Survival in Resected Node-Negative Small Bowel Adenocarcinoma: a SEER Database Analysis

Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma

OBJECTIVES

Radiomics has been demonstrated to be strongly associated with TNM stage and patient prognosis. We aimed to develop a model for predicting lymph node metastasis (LNM) and survival.

METHODS

For radiomics texture selection, 3D Slicer 5.0.3 software and the least absolute shrinkage and selection operator (LASSO) algorithm were used. Subsequently, the radiomics model, computed tomography (CT) image, and clinical risk model were compared. The performance of the three models was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration plots, and clinical impact curves (CICs).

RESULTS

For the LNM prediction model, 224 patients with LNM information were used to construct a model that was applied to predict LNM. According to the CT data and clinical characteristics, we constructed a radiomics model, CT imaging model and clinical model. The radiomics model for evaluating LNM status showed excellent calibration and discrimination in the training cohort (AUC = 0.926, 95% CI = 0.869-0.982) and the validation cohort (AUC = 0.872, 95% CI = 0.802-0.941). DeLong's test demonstrated that the difference among the three models was significant. Similarly, DCA and CIC showed that the radiomics model has better clinical utility than the CT imaging model and clinical model. Our model also exhibited good performance in predicting survival-in line with the findings of the model built with clinical risk factors.

CONCLUSIONS

CT radiomics models exhibited better predictive performance for LNM than models built based on clinical risk characteristics and CT imaging and had comparative clinical utility for predicting patient prognosis.

CRITICAL RELEVANCE STATEMENT

The radiomics model showed excellent performance and discrimination for predicting LNM and survival of duodenal papillary carcinoma (DPC).

KEY POINTS

LNM status determines the most appropriate treatment for DPC. Our radiomics model for evaluating the LNM status of DPC performed excellently. The radiomics model had high sensitivity and specificity for predicting survival, exhibiting great clinical value.

Localized Small Bowel Adenocarcinoma Management: Evidence Summary

Small bowel cancers are rare diseases whose prognosis is poorer than that of colon cancers. Due to disease rarity, there is little data on small bowel adenocarcinoma (SBA) treatment, and most recommendations come from expert agreements or analogies to the management of colon cancer. Although relatively high rates of local recurrence are observed for duodenal malignancies, distant metastatic relapse remains common and requires adjuvant systemic therapy. Given the similarities between SBA and colorectal cancer, radiotherapy and chemotherapy strategies used for the latter disease are frequently pursued for the former disease, specifically for tumors located in the duodenum. However, no previous randomized study has evaluated the benefit of adjuvant chemotherapy on the overall survival of SBA patients. Most previous studies on treatment outcomes and prognostic factors in this context were based on large international databases, such as the Surveillance, Epidemiology, and End Results or the National Cancer Database. Studies are required to establish and validate prognostic and predictive markers relevant in this context to inform the use of (neo) adjuvant treatment. Among those, deficient mismatch repair tumors represent 20% of SBAs, but their impact on chemosensitivity remains unknown. Herein, we summarize the current evidence on the management of localized SBA, including future perspectives.