Developing a predictive nomogram and web-based survival calculator for locally advanced hypopharyngeal cancer: A propensity score-adjusted, population-based study.

Understanding the clinical features and accurately predicting the prognosis of patients with locally advanced hypopharyngeal squamous cell carcinoma (LA-HPSCC) is important for patient centered decision-making. This study aimed to create a multi-factor nomogram predictive model and a web-based calculator to predict post-therapy survival for patients with LA-HPSCC. A retrospective cohort study analyzing Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 for patients diagnosed with LA-HPSCC was conducted and randomly divided into a training and a validation group (7:3 ratio). The external validation cohort included 276 patients from Sichuan Cancer Hospital, China. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis was used to identify independent factors associated with overall survival (OS) and cancer-specific survival (CSS), and nomogram models and web-based survival calculators were constructed. Propensity score matching (PSM) was used to compare survival with different treatment options. A total of 2526 patients were included in the prognostic model. The median OS and CSS for the entire cohort were 20 (18.6-21.3) months and 24 (21.7-26.2) months, respectively. Nomogram models integrating the seven factors demonstrated high predictive accuracy for 3-year and 5-year survival. PSM found that patients who received surgery-based curative therapy had better OS and CSS than those who received radiotherapy-based treatment (median survival times: 33 months vs 18 months and 40 months vs 22 months, respectively). The nomogram model accurately predicted patient survival from LA-HPSCC. Surgery with adjuvant therapy yielded significantly better survival than definitive radiotherapy. and should be prioritized over definitive radiotherapy.

Lymph drainage and cervical fascia anatomy-oriented differential nodal CTV delineation at the supraclavicular region for esophageal cancer and nasopharyngeal cancer.

PURPOSE: To determine the differences in supraclavicular lymph node metastasis between esophageal cancer (EC) and nasopharyngeal cancer (NPC) and explore the feasibility of differential supraclavicular clinical target volume (CTV) contouring between these two diseases based on the involvement of different fascial spaces. MATERIALS AND METHODS: One hundred patients with supraclavicular nodes positive for EC or NPC were enrolled, and their pre-treatment images were reviewed. The distribution patterns of nodes between the two diseases were compared in the context of node levels defined by the 2017 Japanese Esophageal Society and 2013 International Consensus on Cervical Lymph Node Level Classification. Grouping supraclavicular nodes based on sub-compartments formed by the cervical fascia was discussed, and the feasibility of differential CTV contouring based on the differences in the involvement of these sub-compartments between EC and NPC was explored. RESULTS: The 2013 Consensus on cervical node levels and 2017 Japanese Esophageal Society node station could not practically guide supraclavicular CTV contouring. We divided the supraclavicular space into six sub-compartments: the para-esophageal space (PES), carotid sheath space (CSS), sub-thyroid pre-trachea space (STPTS), pre-vascular space (PVS), and vascular lateral space (VLS) I and II. EC mainly spread to the PES, STPTS, CSS, and VLS I, whereas NPC tended to spread to the CSS, VLS I, and VLS II. These combinations of sub-compartments may help constitute the supraclavicular CTVs for EC and NPC. CONCLUSIONS: The fascia anatomy-based sub-compartments sufficiently distinguished metastasis to the supraclavicular space between EC and NPC, thus facilitating differential CTV contouring between these two diseases.

Establishment of predictive nomogram and web-based survival risk calculator for malignant pleural mesothelioma: A SEER database analysis.

Background: Malignant pleural mesothelioma (MPM) is an uncommon condition with limited available therapies and dismal prognoses. The purpose of this work was to create a multivariate clinical prognostic nomogram and a web-based survival risk calculator to forecast patients' prognoses. Methods: Using a randomization process, training and validation groups were created for a retrospective cohort study that examined the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 for individuals diagnosed with MPM (7:3 ratio). Overall survival (OS) and cancer-specific survival (CSS) were the primary endpoints. Clinical traits linked to OS and CSS were identified using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, which was also utilized to develop nomogram survival models and online survival risk calculators. By charting the receiver operating characteristic (ROC), consistency index (C-index), calibration curve, and decision curve analysis (DCA), the model's performance was assessed. The nomogram was used to classify patients into various risk categories, and the Kaplan-Meier method was used to examine each risk group's survival rate. Results: The prognostic model comprised a total of 1978 patients. For the total group, the median OS and CSS were 10 (9.4-10.5) and 11 (9.4-12.6) months, respectively. As independent factors for OS and CSS, age, gender, insurance, histology, T stage, M stage, surgery, and chemotherapy were chosen. The calibration graphs demonstrated good concordance. In the training and validation groups, the C-indices for OS and CSS were 0.729, 0.717, 0.711, and 0.721, respectively. Our nomogram produced a greater clinical net benefit than the AJCC 7th edition, according to DCA and ROC analysis. According to the cut-off values of 171 for OS and 189 for CSS of the total scores from our nomogram, patients were classified into two risk groups. The P-value < 0.001 on the Kaplan-Meier plot revealed a significant difference in survival between the two patient groups. Conclusions: Patient survival in MPM was correctly predicted by the risk evaluation model. This will support clinicians in the practice of individualized medicine.