A novel nomogram and risk classification system for predicting overall survival in head and neck squamous cell cancer with distant metastasis at initial diagnosis

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database

BACKGROUND AND AIM

In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression.

METHODS

In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model.

RESULTS

In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival.

CONCLUSIONS

Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

Distant metastasis at the time of presentation of head and neck squamous cell carcinoma: a retrospective chart review from a tertiary cancer care centre

OBJECTIVE

To evaluate the rates and patterns of distant metastasis in head and neck SCC at the time of presentation and to study the association between distant metastasis with pre-treatment, clinical, and pathological predictors of outcomes.

METHOD

This is a retrospective study conducted in a tertiary care hospital. All patients with primary head and neck squamous cell carcinoma that had been evaluated at our institute between October 2018 and December 2020 were included in the study. Various clinical data were analysed and pattern of metastasis was studied.

RESULT

Ten per cent (50 cases) of 501 studied patients had distant metastasis. The most common site of distant metastasis was lung. The rate of distant metastasis was high in patients with poorly differentiated cancers. By Kaplan-Meier analysis, the median survival duration after diagnosis of metastasis was four months.

CONCLUSION

The rate of distant metastasis was 10 per cent in the study. Patients with poorly differentiated tumours, locally advanced primary lesions, higher nodal stage, particularly with extra nodal extension, and hypopharyngeal primary, tend to exhibit increased risk for distant metastasis at the time of presentation.

Diagnostic and prognostic nomograms for laryngeal carcinoma patients with lung metastasis: a SEER-based study

PURPOSE

To establish two nomograms to quantify the risk of lung metastasis (LM) in laryngeal carcinoma (LC) and predict the overall survival of LC patients with LM.

METHODS

Totally 9515 LC patients diagnosed histologically from 2000 to 2019 were collected from the Surveillance, Epidemiology, and End Results database. The independent diagnostic factors for LM in LC patients and prognostic factors for LC patients with LM were identified by logistic and Cox regression analysis, respectively. Nomograms were established based on regression coefficients and evaluated by receiver operating characteristic curve, calibration curves, and decision curve analysis.

RESULTS

Patients with supraglottis, higher pathological grade, higher N stage, and distant metastasis (bone, brain, or liver) were more likely to have LM (P < 0.05). Chemotherapy, surgery and radiotherapy were independent factors of the overall survival of LC patients with LM (P < 0.05). The area under curve of diagnostic nomogram were 0.834 and 0.816 in the training and validation cohort respectively. For the prognostic nomogram, the area under curves of 1-, 2-, and 3-years were 0.735, 0.734, and 0.709 in the training cohort and 0.705, 0.803, and 0.809 in the validation cohort. The calibration curves and decision curve analysis indicated good performance of the nomograms.

CONCLUSION

Distant metastasis (bone, brain, or liver) and N stage should be considered for prediction of LM in LC patients. Chemotherapy is the most significant influencing prognostic factor improving the survival of LC patients with LM. Two nomograms may benefit for providing better precautionary measures and treatment decision.

Survival Benefit from Cancer-Directed Surgery for Metastatic Head and Neck Cancer

OBJECTIVES

This study aimed to gather evidence for the survival benefit of cancer-directed surgery (CDS) in metastatic head and neck cancer (M1 HNC) and identify which patients will benefit most from CDS.

METHODS

Patients with M1 HNC were identified within the SEER database. According to whether received CDS, patients were divided into the CDS and non-CDS groups. The bias between the two groups was minimized using Propensity Score Matching (PSM), and the prognostic role of CDS was investigated using Kaplan-Meier analysis, log-rank test, and Cox proportional hazard models. The primary endpoint was overall survival (OS), and the secondary endpoint was cancer-specific survival (CSS).

RESULTS

A total of 3215 patients with M1 HNC were extracted, including 566 patients who received CDS that were 1:1 propensity score-matched with patients who did not receive CDS. In the matched dataset, the median OS and CSS in CDS groups were significantly higher than in non-CDS groups (OS: 19.0 vs. 9.0 months, p < 0.001; CSS: 21.0 vs. 9.0 months, p < 0.001). Meanwhile, multivariable Cox regression analysis also revealed that CDS was a favorable prognostic factor for both OS and CSS. Furthermore, subgroups of patients with M1 HNC (younger age, being married, grade I-II, oropharynx site, earlier T/N stage, radiotherapy) were inclined to benefit from CDS, while those patients who received chemotherapy failed to benefit from CDS.

CONCLUSIONS

This study indicated that CDS was associated with improved survival in M1 HNC, especially for those subpopulations that benefit more from CDS treatment.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:1288-1298, 2024.

Engineering models of head and neck and oral cancers on-a-chip

Head and neck cancers (HNCs) rank as the sixth most common cancer globally and result in over 450 000 deaths annually. Despite considerable advancements in diagnostics and treatment, the 5-year survival rate for most types of HNCs remains below 50%. Poor prognoses are often attributed to tumor heterogeneity, drug resistance, and immunosuppression. These characteristics are difficult to replicate using in vitro or in vivo models, culminating in few effective approaches for early detection and therapeutic drug development. Organs-on-a-chip offer a promising avenue for studying HNCs, serving as microphysiological models that closely recapitulate the complexities of biological tissues within highly controllable microfluidic platforms. Such systems have gained interest as advanced experimental tools to investigate human pathophysiology and assess therapeutic efficacy, providing a deeper understanding of cancer pathophysiology. This review outlines current challenges and opportunities in replicating HNCs within microphysiological systems, focusing on mimicking the soft, glandular, and hard tissues of the head and neck. We further delve into the major applications of organ-on-a-chip models for HNCs, including fundamental research, drug discovery, translational approaches, and personalized medicine. This review emphasizes the integration of organs-on-a-chip into the repertoire of biological model systems available to researchers. This integration enables the exploration of unique aspects of HNCs, thereby accelerating discoveries with the potential to improve outcomes for HNC patients.

Development and Validation of Nomograms to Predict Risk and Prognosis in Salivary Gland Carcinoma Patient with Distant Metastases

Background: Salivary gland carcinoma (SGC) patients with distant metastasis (DM) are rare, and understanding this disease is insufficient. Nomograms can predict the prognostic probability of patients, while few studies have examined diagnostic and prognostic factors in SGC patients with DM. The purpose of this study was to establish and validate the risk and prognostic nomograms of SGC patients with DM. Methods: Based on the SEER database, we analyzed the data of SGC patients between 2004 and 2015. Logistic regression analyses and Cox proportional hazards regression analyses were used to identify risk and prognostic factors for DM in SGC patients. Based on the Akaike information criterion (AIC) value and likelihood ratio test, the best-fitting model was selected to build risk and prognostic nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Kaplan-Meier (K-M) survival curves. ROC curves were also used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system. Results: 7418 SGC patients were included in the study, and 307 (4.14%) of them were diagnosed with DM. This study identified that there are variables (age ≥ 80, no-parotid gland primary site, histologic type of mucoepidermoid carcinoma and squamous cell carcinoma, T stage ≥ T2, N staged ≥ N1, histologic grade ≥ III, and tumor size ≥ 41 mm) associated with the occurrence of DM in SGC patients. Therefore, we constructed diagnostic and prognostic nomograms after incorporating these variables. ROC curves illustrated the better predictive efficacy of 2 nomograms over the AJCC staging system. DCA curves, calibration curves, and K-M survival curves showed that 2 nomograms can accurately predict the occurrence and prognosis of DM among SGC patients in training and validation sets. Conclusion: It was shown that the nomograms were highly discriminative in predicting the diagnosis and prognosis of SGC patients with DM, and could identify high-risk patients, thereby providing SGC patients with individualized treatment plans.

A Random Forest Model for Post-Treatment Survival Prediction in Patients with Non-Squamous Cell Carcinoma of the Head and Neck

BACKGROUND

Compared to squamous cell carcinoma, head and neck non-squamous cell carcinoma (HNnSCC) is rarer. Integrated survival prediction tools are lacking.

METHODS

4458 patients of HNnSCC were collected from the SEER database. The endpoints were overall survivals (OSs) and disease-specific survivals (DSSs) of 3 and 5 years. Cases were stratified-randomly divided into the train & validation (70%) and test cohorts (30%). Tenfold cross validation was used in establishment of the model. The performance was evaluated with the test cohort by the receiver operating characteristic, calibration, and decision curves.

RESULTS

The prognostic factors found with multivariate analyses were used to establish the prediction model. The area under the curve (AUC) is 0.866 (95%CI: 0.844-0.888) for 3-year OS, 0.862 (95%CI: 0.842-0.882) for 5-year OS, 0.902 (95%CI: 0.888-0.916) for 3-year DSS, and 0.903 (95%CI: 0.881-0.925) for 5-year DSS. The net benefit of this model is greater than that of the traditional prediction methods. Among predictors, pathology, involved cervical nodes level, and tumor size are found contributing the most variance to the prediction. The model was then deployed online for easy use.

CONCLUSIONS

The present study incorporated the clinical, pathological, and therapeutic features comprehensively and established a clinically effective survival prediction model for post-treatment HNnSCC patients.