Individualized Dynamic Prediction Model for Patient-Reported Voice Quality in Early-Stage Glottic Cancer

Objective: Early-stage glottic cancer (ESGC) is a malignancy of the head and neck. Besides disease control, preservation and improvement of voice quality are essential. To enable expectation management and well-informed decision-making, patients should be sufficiently counseled with individualized information on expected voice quality. This study aims to develop an individualized dynamic prediction model for patient-reported voice quality. This model should be able to provide individualized predictions at every time point from intake to the end of follow-up. Study Design: Longitudinal cohort study. Setting: Tertiary cancer center. Methods: Patients treated for ESGC were included in this study (N = 294). The Voice Handicap Index was obtained prospectively. The framework of mixed and joint models was used. The prognostic factors used are treatment, age, gender, comorbidity, performance score, smoking, T-stage, and involvement of the anterior commissure. The overall performance of these models was assessed during an internal cross-validation procedure and presentation of absolute errors using box plots. Results: The mean age in this cohort was 67 years and 81.3% are male. Patients were treated with transoral CO2 laser microsurgery (57.8%), single vocal cord irradiation up to (24.5), or local radiotherapy (17.5%). The mean follow-up was 43.4 months (SD 21.5). Including more measurements during prediction improves predictive performance. Including more clinical and demographic variables did not provide better predictions. Little differences in predictive performance between models were found. Conclusion: We developed a dynamic individualized prediction model for patient-reported voice quality. This model has the potential to empower patients and professionals in making well-informed decisions and enables tailor-made counseling.</p

Integrated prediction and decision models are valuable in informing personalized decision making

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A cause-specific Cox model for second primary tumors in patients with head and neck cancer: A RONCDOC study

Background: The aim of this study was to identify risk factors for the development of second primary tumors (SPTs) in the head and neck region, lungs, and esophagus in patients with head and neck cancer. Methods: We collected data from 1581 patients. A cause-specific Cox model for the development of an SPT was fitted, accounting for the competing risks residual/recurrent tumor and mortality. Results: Of all patients, 246 (15.6%) developed SPTs. Analysis showed that tobacco and alcohol use, comorbidity, and the oral cavity subsite were risk factors for SPTs. The C-index, the discriminative accuracy, of the model for SPT was 0.65 (95% confidence interval, 0.61–0.68). Conclusions: Our results show that there is potential to identify patients who have an increased risk to develop an SPT. This might increase their survival chances and quality of life. More research is needed to provide head and neck clinicians with definitive recommendations

Dissemination patterns and chronology of distant metastasis affect survival of patients with head and neck squamous cell carcinoma

Objectives: To define metastatic categories based on their prognostic significance. We hypothesized that oligometastasis in patients with head and neck squamous cell carcinoma (HNSCC) is associated with better post-distant metastasis disease specific survival (post-DM DSS) compared to patients with polymetastasis. Furthermore, the impact on survival of synchronous versus metachronous distant metastasis (DM) occurrence was assessed. Materials and methods: Retrospective cohort study in which patients with DM were stratified into three groups: oligometastasis (maximum of 3 metastatic foci in ≤2 anatomic sites), explosive metastasis (≥4 metastatic foci at one anatomic site) and explosive-disseminating metastasis (spread to ≥3 anatomic sites or >3 metastatic foci in 2 anatomic sites). In addition, patients were divided into synchronous versus metachronous DM. Results: Between January 1, 2006 and December 31, 2013, a total of 2687 patients with HNSCC were identified, of which 324 patients developed DM. In this group, 115 (35.5%) patients had oligometastasis, 64 (19.8%) patients had explosive metastasis and 145 (44.8%) patients had explosive-disseminating metastasis. Their median post-DM DSS were 4.7 months, 4.1 months and 1.7 months respectively (p <.001). Synchronous DM was associated with more favorable survival rates in univariable and multivariable analyses than metachronous DM with recurrence of the index tumor (6-month post-DM DSS probability of 0.51 vs 0.17, p <.001). Conclusion: Oligometastasis in HNSCC signifies a better prognosis than a polymetastatic pattern. Metachronous DM occurrence with recurrence of the primary index tumor is associated with an unfavorable prognosis

Development and Assessment of a Model for Predicting Individualized Outcomes in Patients with Oropharyngeal Cancer

Importance: Recent insights into the biologic characteristics and treatment of oropharyngeal cancer may help inform improvements in prognostic modeling. A bayesian multistate model incorporates sophisticated statistical techniques to provide individualized predictions of survival and recurrence outcomes for patients with newly diagnosed oropharyngeal cancer. Objective: To develop a model for individualized survival, locoregional recurrence, and distant metastasis prognostication for patients with newly diagnosed oropharyngeal cancer, incorporating clinical, oncologic, and imaging data. Design, Setting, and Participants: In this prognostic study, a data set was used comprising 840 patients with newly diagnosed oropharyngeal cancer treated at a National Cancer Institute-designated center between January 2003 and August 2016; analysis was performed between January 2019 and June 2020. Using these data, a bayesian multistate model was developed that can be used to obtain individualized predictions. The prognostic performance of the model was validated using data from 447 patients treated for oropharyngeal cancer at Erasmus Medical Center in the Netherlands. Exposures: Clinical/oncologic factors and imaging biomarkers collected at or before initiation of first-line therapy. Main Outcomes and Measures: Overall survival, locoregional recurrence, and distant metastasis after first-line cancer treatment. Results: Of the 840 patients included in the National Cancer Institute-designated center, 715 (85.1%) were men and 268 (31.9%) were current smokers. The Erasmus Medical Center cohort comprised 300 (67.1%) men, with 350 (78.3%) current smokers. Model predictions for 5-year overall survival demonstrated good discrimination, with area under the curve values of 0.81 for the model with and 0.78 for the model without imaging variables. Application of the model without imaging data in the independent Dutch validation cohort resulted in an area under the curve of 0.75. This model possesses good calibration and stratifies patients well in terms of likely outcomes among many competing events. Conclusions and Relevance: In this prognostic study, a multistate model of oropharyngeal cancer incorporating imaging biomarkers appeared to estimate and discriminate locoregional recurrence from distant metastases. Providing personalized predictions of multiple outcomes increases the information available for patients and clinicians. The web-based application designed in this study may serve as a useful tool for generating predictions and visualizing likely outcomes for a specific patient.