Risk prediction of occult lymph node metastasis in patients with clinical T1 through T2 N0 esophageal squamous cell carcinoma

Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era

Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer with a poor prognosis. Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients. With the advancement of artificial intelligence (AI) technology and the proliferation of medical digital information, AI has demonstrated promising sensitivity and accuracy in assisting precise detection, treatment decision-making, and prognosis assessment of ESCC. It has become a unique opportunity to enhance comprehensive clinical management of ESCC in the era of precision oncology. This review examines how AI is applied to the diagnosis, treatment, and prognosis assessment of ESCC in the era of precision oncology, and analyzes the challenges and potential opportunities that AI faces in clinical translation. Through insights into future prospects, it is hoped that this review will contribute to the real-world application of AI in future clinical settings, ultimately alleviating the disease burden caused by ESCC.

Prognostic factors for patients with pathologic T1-T2N+ esophageal squamous cell carcinoma: A retrospective study with external validation

BACKGROUND

Lymph node metastasis is significantly associated with a worse prognosis in patients with localized early-stage esophageal squamous cell carcinoma. This study aimed to explore the prognostic factors and develop a nomogram for predicting survival in patients with pathologic T1-2N+ esophageal squamous cell carcinoma.

METHODS

Between 2014 and 2022, patients with pT1-2N+ esophageal squamous cell carcinoma who underwent esophagectomy with lymphadenectomy at 2 institutes were reviewed and assigned to training and external validation cohorts. Independent prognostic factors were identified via univariate and multivariate Cox regression analyses. The nomogram model was developed and evaluated by the area under the receiver operating characteristic curve and calibration curve.

RESULTS

In total, 268 patients with a median age of 65 years (range, 40-82) were included and assigned to training (n = 190) and external validation (n = 78) cohorts. The Cox proportional hazards model demonstrated that body mass index (P = .031), surgical approach (P < .001), T stage (P = .015), and Clavien-Dindo classification (P < .001) were independent prognostic factors in the training cohort. The nomogram showed good discrimination, with an area under the receiver operating characteristic curve for 1-year, 3-year, and 5-year of 0.810, 0.789, and 0.809 in the training cohort and 0.782, 0.679, and 0.698 in the validation cohort. The calibration curve showed that the predicted survival probability was in good agreement with the actual survival probability.

CONCLUSION

Lower body mass index, left surgical approach, T2 stage, and Clavien-Dindo classification grade III to V were related to worse prognosis in patients with pT1-T2N+ esophageal squamous cell carcinoma. The developed nomogram may predict individual survival accurately.

Research Progress on the Predicting Factors and Coping Strategies for Postoperative Recurrence of Esophageal Cancer

Esophageal cancer is one of the malignant tumors with poor prognosis in China. Currently, the treatment of esophageal cancer is still based on surgery, especially in early and mid-stage patients, to achieve the goal of radical cure. However, esophageal cancer is a kind of tumor with a high risk of recurrence and metastasis, and locoregional recurrence and distant metastasis are the leading causes of death after surgery. Although multimodal comprehensive treatment has advanced in recent years, the prediction, prevention and treatment of postoperative recurrence and metastasis of esophageal cancer are still unsatisfactory. How to reduce recurrence and metastasis in patients after surgery remains an urgent problem to be solved. Given the clinical demand for early detection of postoperative recurrence of esophageal cancer, clinical and basic research aiming to meet this demand has been a hot topic, and progress has been observed in recent years. Therefore, this article reviews the research progress on the factors that influence and predict postoperative recurrence of esophageal cancer, hoping to provide new research directions and treatment strategies for clinical practice.

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma

Background

For patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC.

Methods

Patients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model’s generalizability and effectiveness. To evaluate prediction performance, the area under the receiver operating characteristic curve (AUC) was applied.

Results

Of the 1097 included patients, 294 (26.8%) had LNM. The ML models based on clinical features showed good predictive performance for LNM status, with a median bootstrapped AUC of 0.659 (range: 0.592, 0.715). The optimal model using the naive Bayes algorithm with feature selection by determination coefficient had the highest AUC of 0.715 (95% CI: 0.671, 0.763). In the external test set, the optimal ML model achieved an AUC of 0.752 (95% CI: 0.674, 0.829), which was superior to that of T stage (0.624, 95% CI: 0.547, 0.701).

Conclusions

ML models provide good LNM prediction value for stage T1-T2 ESCC patients, and the naive Bayes algorithm with feature selection by determination coefficient performed best.