Comparative Morphometrics of the Sacral Vertebra in \u3cem\u3eAneides\u3c/em\u3e (Caudata: Plethodontidae).

The genus Aneides (Caudata: Plethodontidae) is an arboreal salamander with a prehensile tail and a distribution that spans North America. It is hypothesized that adaptations for arboreality will be visible in the osteology of the sacral vertebra either by qualitative analysis or linear and morphometric analysis in comparison with other plethodontid salamanders. This study demonstrates that while qualitative and quantitative analyses are successful at making genus-level distinctions between taxa, identification to lower taxonomic levels remains inconclusive. Linear morphometrics and dorsal Procrustes landmarks were the most successful metrics to identify known taxa. Two unidentified fossil salamander sacral vertebrae from Oregon Caves National Monument are examined with the same techniques and are tentatively identified as Hydromantes based on qualitative similarities to modern Hydromantes specimens, as the quantitative analyses were unable to confidently diagnose the unknown specimens

An efficient strategy to select head and neck cancer patients for adaptive radiotherapy

BACKGROUND AND PURPOSE: Adaptive radiotherapy (ART) is workload intensive but only benefits a subgroup of patients. We aimed to develop an efficient strategy to select candidates for ART in the first two weeks of head and neck cancer (HNC) radiotherapy.MATERIALS AND METHODS: This study retrospectively enrolled 110 HNC patients who underwent modern photon radiotherapy with at least 5 weekly in-treatment re-scan CTs. A semi auto-segmentation method was applied to obtain the weekly mean dose (D mean) to OARs. A comprehensive NTCP-profile was applied to obtain NTCP's. The difference between planning and actual values of D mean (ΔD mean) and dichotomized difference of clinical relevance (BIOΔNTCP) were used for modelling to determine the cut-off maximum ΔD mean of OARs in week 1 and 2 (maxΔD mean_1 and maxΔD mean_2). Four strategies to select candidates for ART, using cut-off maxΔD mean were compared. RESULTS: The Spearman's rank correlation test showed significant positive correlation between maxΔD mean and BIOΔNTCP (p-value &lt;0.001). For major BIOΔNTCP (&gt;5%) of acute and late toxicity, 10.9% and 4.5% of the patients were true candidates for ART. Strategy C using both cut-off maxΔD mean_1 (3.01 and 5.14 Gy) and cut-off maxΔD mean_2 (3.41 and 5.30 Gy) showed the best sensitivity, specificity, positive and negative predictive values (0.92, 0.82, 0.38, 0.99 for acute toxicity and 1.00, 0.92, 0.38, 1.00 for late toxicity, respectively). CONCLUSIONS: We propose an efficient selection strategy for ART that is able to classify the subgroup of patients with &gt;5% BIOΔNTCP for late toxicity using imaging in the first two treatment weeks.</p

Impact of radiation-induced toxicities on quality of life of patients treated for head and neck cancer

PURPOSE: The aim of this study is to establish the relative impact of physician-rated toxicities and patient-rated symptoms in head and neck cancer (HNC) on quality of life (QOL) and to weigh the various toxicities and symptoms during treatment plan optimization and selection. MATERIALS AND METHODS: This prospective cohort study comprised 1,083 HNC patients (development: 750, validation: 333) treated with definitive radiotherapy with or without chemotherapy. Clinical factors were scored at baseline. Physician-rated and patient-rated outcome measures and QOL (EORTC QLQ-HN35 and QLQ-C30) were prospectively scored at baseline and 6, 12, 18 and 24 months after radiotherapy. The impact of 20 common toxicities and symptoms (related to swallowing, salivary function, speech, pain and general complaints) on QOL (0-100 scale) was established for each time point by combining principal component analysis and multivariable linear regression. RESULTS: Radiation-induced toxicities and symptoms resulted in a significant decline in QOL of patients with 12.4±12.8 points at 6 months to 16.6±17.1 points at 24 months. The multivariable linear models described the QOL points subtracted for each toxicity and symptom after radiotherapy. For example, xerostomia and weight loss had a significant but minor effect (on average -0.5 and -0.6 points) while speech problems and fatigue had a much greater impact (on average -11.9 and -17.4 points) on QOL. R2 goodness-of-fit values for the QOL models ranged from 0.64 (6 months) to 0.72 (24 months). CONCLUSION: The relative impact of physician-rated toxicities and patient-rated symptoms on QOL was quantified and can be used to optimize, compare and select HNC radiotherapy treatment plans, to balance the relevance of toxicities and to achieve the best QOL for individual patients

Quality of life and toxicity guided treatment plan optimisation for head and neck cancer

PURPOSE: To evaluate the feasibility of semi-automatic Quality of Life (QOL)-weighted normal tissue complication probability (NTCP)-guided VMAT treatment plan optimisation in head and neck cancer (HNC) and compare predicted QOL to that obtained with conventional treatment. MATERIALS AND METHODS: This study included 30 HNC patients who were treated with definitive radiotherapy. QOL-weighted NTCP-guided VMAT plans were optimised directly on 80 multivariable NTCP models of 20 common toxicities and symptoms on 4 different time points (6, 12, 18 and 24 months after radiotherapy) and each NTCP model was weighted relative to its impact on QOL. Planning results, NTCP and predicted QOL were compared with the clinical conventional VMAT plans. RESULTS: QOL-weighted NTCP-guided VMAT plans were clinically acceptable, had target coverage equally adequate as the clinical plans, but prioritised sparing of organs at risk (OAR) related to toxicities and symptoms that had the highest impact on QOL. NTCP was reduced for, e.g., dysphagia (-6.1% for ≥ grade 2/ -7.6% for ≥ grade 3) and moderate-to-severe fatigue / speech problems / hoarseness (-0.7%/ -1.5%/ -2.5%) at 6 months, respectively. Concurrently, the average NTCP of toxicities related to salivary function increased with +0.4% to +5.7%. QOL-weighted NTCP-guided plans were produced in less time, were less dependent on the treatment planner experience and yielded more consistent results. The average predicted QOL improved by 0.7, 0.9, 1.0, and 1.1 points on a 0-100 scale (p < 0.001) at 6, 12, 18, and 24 months, respectively, compared to the clinical plans. CONCLUSION: Semi-automatic QOL-weighted NTCP-guided VMAT treatment plan optimisation is feasible. It prioritised sparing of OARs related to high-impact toxicities and symptoms and resulted in a systematic improvement of predicted QOL compared to conventional VMAT

Key challenges in normal tissue complication probability model development and validation:towards a comprehensive strategy

Normal Tissue Complication Probability (NTCP) models can be used for treatment plan optimisation and patient selection for emerging treatment techniques. We discuss and suggest methodological approaches to address key challenges in NTCP model development and validation, including: missing data, non-linear response relationships, multicollinearity between predictors, overfitting, generalisability and the prediction of multiple complication grades at multiple time points. The methodological approaches chosen are aimed to improve the accuracy, transparency and robustness of future NTCP-models. We demonstrate our methodological approaches using clinical data

The relation between prediction model performance measures and patient selection outcomes for proton therapy in head and neck cancer

Background: Normal-tissue complication probability (NTCP) models predict complication risk in patients receiving radiotherapy, considering radiation dose to healthy tissues, and are used to select patients for proton therapy, based on their expected reduction in risk after proton therapy versus photon radiotherapy (ΔNTCP). Recommended model evaluation measures include area under the receiver operating characteristic curve (AUC), overall calibration (CITL), and calibration slope (CS), whose precise relation to patient selection is still unclear. We investigated how each measure relates to patient selection outcomes. Methods: The model validation and consequent patient selection process was simulated within empirical head and neck cancer patient data. By manipulating performance measures independently via model perturbations, the relation between model performance and patient selection was studied. Results: Small reductions in AUC (-0.02) yielded mean changes in ΔNTCP between 0.9–3.2 %, and single-model patient selection differences between 2–19 %. Deviations (-0.2 or +0.2) in CITL or CS yielded mean changes in ΔNTCP between 0.3–1.4 %, and single-model patient selection differences between 1–10 %. Conclusions: Each measure independently impacts ΔNTCP and patient selection and should thus be assessed in a representative sufficiently large external sample. Our suggested practical model selection approach is considering the model with the highest AUC, and recalibrating it if needed

Patient-Reported Toxicity and Quality-of-Life Profiles in Patients With Head and Neck Cancer Treated With Definitive Radiation Therapy or Chemoradiation

Purpose: Radiation therapy is an effective but burdensome treatment for head and neck cancer (HNC). We aimed to characterize the severity and time pattern of patient-reported symptoms and quality of life in a large cohort of patients with HNC treated with definitive radiation therapy, with or without systemic treatment. Methods and Materials: A total of 859 patients with HNC treated between 2007 and 2017 prospectively completed the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Head and Neck Cancer module (QLQ-HN35) and Core Quality of Life Questionnaire (QLQ-C30) at regular intervals during and after treatment for up to 5 years. Patients were classified into 3 subgroups: early larynx cancer, infrahyoideal cancer, and suprahyoideal cancer. Outcome scales of both questionnaires were quantified per subgroup and time point by means of average scores and the frequency distribution of categorized severity (none, mild, moderate, and severe). Time patterns and symptom severity were characterized. Toxicity profiles were compared using linear mixed model analysis. Additional toxicity profiles based on age, human papillomavirus status, treatment modality, smoking status, tumor site, and treatment period were characterized as well. Results: The study population consisted of 157 patients with early larynx cancer, 304 with infrahyoideal cancer, and 398 with suprahyoideal cancer. The overall questionnaire response rate was 83%. Generally, the EORTC QLQ-HN35 symptoms reported showed a clear time pattern, with increasing scores during treatment followed by a gradual recovery in the first 2 years. Distinct toxicity profiles were seen across subgroups (P < .001), with generally less severe symptom scores in the early larynx subgroup. The EORTC QLQ-C30 functioning, quality-of-life, and general symptoms reported showed a less evi- dent time pattern and less pronounced differences in mean scores between subgroups, although differences were still signifi- cant (P < .001). Differences in mean scores were most pronounced for role functioning, appetite loss, fatigue, and pain. Conclusions: We established patient-reported toxicity and quality-of-life profiles that showed different patterns for 3 sub-groups of patients with HNC. These profiles provide detailed information on the severity and persistence of various symptoms as experienced by patients during and after definitive radiation therapy. These profiles can be used to inform treatment of future patients and may serve as a benchmark for future studies. (C) 2021 The Authors. Published by Elsevier Inc

Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer:A new concept for individually optimised treatment

Background and purpose: A comprehensive individual toxicity risk profile is needed to improve radiation treatment optimisation, minimising toxicity burden, in head and neck cancer (HNC) patients. We aimed to develop and externally validate NTCP models for various toxicities at multiple time points. Materials and methods: Using logistic regression, we determined the relationship between normal tissue irradiation and the risk of 22 toxicities at ten time points during and after treatment in 750 HNC patients. The toxicities involved swallowing, salivary, mucosal, speech, pain and general complaints. Studied pre-dictors included patient, tumour and treatment characteristics and dose parameters of 28 organs. The resulting NTCP models were externally validated in 395 HNC patients. Results: The NTCP models involved 14 organs that were associated with at least one toxicity. The oral cavity was the predominant organ, associated with 12 toxicities. Other important organs included the parotid and submandibular glands, buccal mucosa and swallowing muscles. In addition, baseline toxicity, treatment modality, and tumour site were common predictors of toxicity. The median discrimination performance (AUC) of the models was 0.71 (interquartile range: 0.68-0.75) at internal validation and 0.67 (interquartile range: 0.62-0.71) at external validation. Conclusion: We established a comprehensive individual toxicity risk profile that provides essential insight into how radiation exposure of various organs translates into multiple acute and late toxicities. This comprehensive understanding of radiation-induced toxicities enables a new radiation treatment optimisation concept that balances multiple toxicity risks simultaneously and minimises the overall tox-icity burden for an individual HNC patient who needs to undergo radiation treatment. (C) 2021 The Author(s). Published by Elsevier B.V