Survival and prognostic factors in patients with synchronous multiple primary esophageal squamous cell carcinoma receiving definitive radiotherapy: A propensity score-matched analysis

Successful triple therapy for advanced synchronous multiple primary esophageal carcinoma with metal stenting, photodynamic and comprehensive systemic therapies-shining light on hope: a case report and literature review

Synchronous multiple primary esophageal cancer (SMPEC) is a rare and aggressive condition often accompanied by obstructive dysphagia, significantly impacting patients' quality of life. Current treatments, including chemotherapy, radiotherapy, immunotherapy, and targeted therapy, are limited in providing immediate symptom relief. This case report describes a 64-year-old female with SMPEC and metastases to thoracic lymph nodes, the lesser curvature of the stomach, and the right adrenal gland, presenting with severe dysphagia (score 4 on the Japanese Dysphagia Severity Scale). To rapidly alleviate symptoms, she underwent simultaneous metal stent implantation and photodynamic therapy (PDT). She started a liquid diet on the second day after treatment and resumed a normal diet one week later. Subsequently, she underwent systemic chemotherapy, targeted therapy, and immunotherapy. By the third treatment cycle, primary and metastatic lesions significantly decreased, achieving a partial response (PR) with stable disease and progression-free survival (PFS) exceeded 12 months. This triple therapy approach-combining stent implantation, PDT, and systemic treatments-proved effective and safe for advanced SMPEC, not only providing immediate dysphagia relief and selective tumor destruction but also delaying disease progression and improving patient outcomes.

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach

BACKGROUND

Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives.

METHOD

We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4. It integrates ResNet-18 and Long Short-Term Memory (LSTM) networks, leveraging their strengths in spatial and sequential data processing. This method uses arterial phase CT scans from ESCC patients. The dataset was meticulously segmented by an experienced radiologist for effective training and validation.

RESULTS

Upon performing five-fold cross-validation, the ResoLSTM-Depth model exhibited commendable performance with an accuracy of 0.857, an AUC of 0.901, a sensitivity of 0.884, and a specificity of 0.828. These results were superior to the ResNet-18 model alone, where the average accuracy is 0.824 and the AUC is 0.879. Attention maps further highlighted influential features for depth prediction, enhancing model interpretability.

CONCLUSION

ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.