Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography

PURPOSE: Classic encoder-decoder-based convolutional neural network (EDCNN) approaches cannot accurately segment detailed anatomical structures of the mandible in computed tomography (CT), for instance, condyles and coronoids of the mandible, which are often affected by noise and metal artifacts. The main reason is that EDCNN approaches ignore the anatomical connectivity of the organs. In this paper, we propose a novel CNN-based 3D mandible segmentation approach that has the ability to accurately segment detailed anatomical structures. METHODS: Different from the classic EDCNNs that need to slice or crop the whole CT scan into 2D slices or 3D patches during the segmentation process, our proposed approach can perform mandible segmentation on complete 3D CT scans. The proposed method, namely, RCNNSeg, adopts the structure of the recurrent neural networks to form a directed acyclic graph in order to enable recurrent connections between adjacent nodes to retain their connectivity. Each node then functions as a classic EDCNN to segment a single slice in the CT scan. Our proposed approach can perform 3D mandible segmentation on sequential data of any varied lengths and does not require a large computation cost. The proposed RCNNSeg was evaluated on 109 head and neck CT scans from a local dataset and 40 scans from the PDDCA public dataset. The final accuracy of the proposed RCNNSeg was evaluated by calculating the Dice similarity coefficient (DSC), average symmetric surface distance (ASD), and 95% Hausdorff distance (95HD) between the reference standard and the automated segmentation. RESULTS: The proposed RCNNSeg outperforms the EDCNN-based approaches on both datasets and yields superior quantitative and qualitative performances when compared to the state-of-the-art approaches on the PDDCA dataset. The proposed RCNNSeg generated the most accurate segmentations with an average DSC of 97.48%, ASD of 0.2170 mm, and 95HD of 2.6562 mm on 109 CT scans, and an average DSC of 95.10%, ASD of 0.1367 mm, and 95HD of 1.3560 mm on the PDDCA dataset. CONCLUSIONS: The proposed RCNNSeg method generated more accurate automated segmentations than those of the other classic EDCNN segmentation techniques in terms of quantitative and qualitative evaluation. The proposed RCNNSeg has potential for automatic mandible segmentation by learning spatially structured information

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review

Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications

Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model

Accurate segmentation of the mandible from cone-beam computed tomography (CBCT) scans is an important step for building a personalized 3D digital mandible model for maxillofacial surgery and orthodontic treatment planning because of the low radiation dose and short scanning duration. CBCT images, however, exhibit lower contrast and higher levels of noise and artifacts due to extremely low radiation in comparison with the conventional computed tomography (CT), which makes automatic mandible segmentation from CBCT data challenging. In this work, we propose a novel coarse-to-fine segmentation framework based on 3D convolutional neural network and recurrent SegUnet for mandible segmentation in CBCT scans. Specifically, the mandible segmentation is decomposed into two stages: localization of the mandible-like region by rough segmentation and further accurate segmentation of the mandible details. The method was evaluated using a dental CBCT dataset. In addition, we evaluated the proposed method and compared it with state-of-the-art methods in two CT datasets. The experiments indicate that the proposed algorithm can provide more accurate and robust segmentation results for different imaging techniques in comparison with the state-of-the-art models with respect to these three datasets

Cardiovascular Disease in Testicular Cancer Survivors:Identification of Risk Factors and Impact on Quality of Life

PURPOSE: Testicular cancer (TC) treatment is clearly associated with cardiovascular morbidity and mortality. To enable development of preventive strategies for cardiovascular disease (CVD), we assessed cardiometabolic risk factors and quality of life (QoL) in TC survivors.METHODS: Incidence of coronary artery disease, myocardial infarction, and heart failure after TC treatment was assessed in a multicenter cohort comprising 4,748 patients treated at the age of 12-50 years between 1976 and 2007. Patients who had developed CVD and a random sample from the cohort (subcohort) received a questionnaire on cardiometabolic risk factors and QoL. A subgroup of responders in the subcohort additionally underwent clinical evaluation of cardiovascular risk factors.RESULTS: After a median follow-up of 16 years, 272 patients had developed CVD. Compared with orchidectomy only, cisplatin combination chemotherapy was associated with an increased CVD risk (hazard ratio [HR], 1.9; 95% CI, 1.1 to 3.1). Patients who were obese or a smoker at diagnosis (HR, 4.6; 95% CI, 2.0 to 10.0 and HR, 1.7; 95% CI, 1.1 to 2.4, respectively), developed Raynaud's phenomenon (HR, 1.9; 95% CI, 1.1 to 3.6) or dyslipidemia (HR, 2.8; 95% CI, 1.6 to 4.7) or had a positive family history for CVD (HR, 2.9; 95% CI, 1.7 to 4.9) had higher CVD risk. More TC survivors with CVD reported inferior QoL on physical domains than survivors who did not develop CVD. Of 304 TC survivors who underwent clinical evaluation for cardiovascular risk factors (median age at assessment: 51 years), 86% had dyslipidemia, 50% had hypertension, and 35% had metabolic syndrome, irrespective of treatment.CONCLUSION: Cardiovascular events in TC survivors impair QoL. Many TC survivors have undetected cardiovascular risk factors. We advocate early lifestyle adjustments and lifelong follow-up with low-threshold treatment of cardiovascular risk factors, especially in obese and smoking patients treated with platinum-based chemotherapy.</p

Frailty and restrictions in geriatric domains are associated with surgical complications but not with radiation-induced acute toxicity in head and neck cancer patients:A prospective study

OBJECTIVES: We aimed to evaluate the association between frailty screening and geriatric assessment (GA) on short term adverse events in patients treated for head and neck cancer (HNC) for the first time in a prospective study. MATERIALS AND METHODS: Newly diagnosed HNC patients undergoing curative treatment were prospectively included in OncoLifeS, a data biobank. Prior to the start of treatment, frailty was assessed with a GA, Groningen Frailty Indicator (GFI) and Geriatric-8 (G8). The GA included comorbidity (Adult Comorbidity Evaluation - 27), nutritional status (Malnutrition Universal Screening Tool), functional status ((instrumental) Activities of Daily Living), mobility (Timed Up & Go), psychological (Geriatric Depression Scale 15) and cognitive (Mini Mental State Examination) measures. Clinically relevant postoperative complications (Clavien-Dindo ≥ grade 2) and acute radiation-induced toxicity (Common Terminology Criteria for Adverse Events version 4.0 ≥ grade 2) were defined as outcome measures. Univariable and multivariable logistic regression analyses were performed, yielding odds ratios (ORs) and 95% confidence intervals (95%CIs). RESULTS: Of the 369 included patients, 259 patients were eligible for analysis. Postoperative complications occurred in 41/148 (27.7%) patients and acute radiation-induced toxicity was present in 86/160 (53.7%) patients. Number of deficit domains of GA (OR = 1.71, 95%CI = 1.14-2.56), GFI (OR = 2.54, 95%CI = 1.02-6.31) and G8 (OR5.59, 95%CI = 2.14-14.60) were associated with postoperative complications, but not with radiation-induced toxicity. CONCLUSION: Frailty and restrictions in geriatric domains were associated with postoperative complications, but not with radiation-induced acute toxicity in curatively treated HNC patients. The results of this prospective study further emphasizes the importance of geriatric evaluation, particularly before surgery

Frailty is associated with decline in health-related quality of life of patients treated for head and neck cancer

Objective: To determine the effect of frailty on Health Related Quality of Life (HRQoL) after treatment for Head and Neck Cancer (HNC). Materials and methods: Patients were prospectively included in OncoLifeS, a data-biobank. Before treatment, patients underwent geriatric screening, including the Groningen Frailty Indicator (GFI) and Geriatric 8 (G8). Patients' HRQoL was measured using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC-QLQ-C30) at three, six, twelve and twenty four months after treatment. Linear mixed models were used for statistical analysis. All models were adjusted for baseline HRQoL values, relevant confounders at baseline and yielded estimates (beta), 95% confidence intervals and p-values. Results: 288 patients were included. The mean age was 68.4 years and 68.8% were male. During follow-up, 84 patients had tumor recurrence and 66 died. Response to EORTC-QLQ-C30 ranged from 77.3% to 87.8%. Frail patients, defined by GFI, had significantly worse Global Health Status/Quality of Life (GHS/QoL) (beta = -8.70(-13.54;-3.86), p <0.001), physical functioning (beta = -4.55(-8.70;-0.40), p <0.032), emotional functioning (beta = -20.06(-25.65;-15.86), p <0.001), and social functioning (beta = -8.44(-13.91;-2.98), p <0.003) three months after treatment compared to non-frail patients. Furthermore, frail patients had a significantly worse course of GHS/QoL (j3 = -7.47(-11.23;-3.70), p = 0.001), physical functioning (beta = -3.28(-6.26;-0.31), p = 0.031) and role functioning (beta = -7.27(-12.26;-2.28), p = 0.005) over time, compared to non-frail patients. When frailty was determined by G8, frailty was significantly associated with worse GHS/QoL (beta = -6.68(-11.00;-2.37), p = 0.003) and emotional functioning (beta = -5.08(-9.43;-0.73), p = 0.022) three months after treatment. Conclusion: Frail patients are at increased risk for decline in HRQoL, and further deterioration during follow-up after treatment for HNC

Association of Deficits Identified by Geriatric Assessment With Deterioration of Health-Related Quality of Life in Patients Treated for Head and Neck Cancer

This cohort study assesses the association of single and accumulated geriatric deficits with health-related quality of life decline after treatment for head and neck cancer. Question Are pretreatment geriatric deficits associated with quality of life after treatment for head and neck cancer? Findings In this cohort study including 283 patients, items of geriatric assessment within all domains (physical, functional, psychological, social) were associated with decline of quality of life after treatment. The accumulation of domains with geriatric deficits was a major significant factor for deterioration at both short- and long-term follow-up after treatment. Meaning Deficits in individual and accumulated geriatric domains are associated with decline in quality of life; this knowledge may aid decision-making, indicate interventions, and reduce loss of quality of life. Importance Accumulation of geriatric deficits, leading to an increased frailty state, makes patients susceptible for decline in health-related quality of life (HRQOL) after treatment for head and neck cancer (HNC). Objective To assess the association of single and accumulated geriatric deficits with HRQOL decline in patients after treatment for HNC. Design, Setting, and Participants Between October 2014 and May 2016, patients at a tertiary referral center were included in the Oncological Life Study (OncoLifeS), a prospective data biobank, and followed up for 2 years. A consecutive series of 369 patients with HNC underwent geriatric assessment at baseline; a cohort of 283 patients remained eligible for analysis, and after 2 years, 189 patients remained in the study. Analysis was performed between March and November 2020. Interventions or Exposures Geriatric assessment included scoring of the Adult Comorbidity Evaluation 27, polypharmacy, Malnutrition Universal Screening Tool, Activities of Daily Living, Instrumental Activities of Daily Living (IADL), Timed Up & Go, Mini-Mental State Examination, 15-item Geriatric Depression Scale, marital status, and living situation. Main Outcomes and Measures The primary outcome measure was the Global Health Status/Quality of Life (GHS/QOL) scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30. Differences between patients were evaluated using linear mixed models at 3 months after treatment (main effects, beta [95% CI]) and declining course per year during follow-up (interaction x time, beta [95% CI]), adjusted for baseline GHS/QOL scores, and age, sex, stage, and treatment modality. Results Among the 283 patients eligible for analysis, the mean (SD) age was 68.3 (10.9) years, and 193 (68.2%) were male. Severe comorbidity (beta = -7.00 [-12.43 to 1.56]), risk of malnutrition (beta = -6.18 [-11.55 to -0.81]), and IADL restrictions (beta = -10.48 [-16.39 to -4.57]) were associated with increased GHS/QOL decline at 3 months after treatment. Severe comorbidity (beta = -4.90 [-9.70 to -0.10]), IADL restrictions (beta = -5.36 [-10.50 to -0.22]), restricted mobility (beta = -6.78 [-12.81 to -0.75]), signs of depression (beta = -7.08 [-13.10 to -1.06]), and living with assistance or in a nursing home (beta = -8.74 [-15.75 to -1.73]) were associated with further GHS/QOL decline during follow-up. Accumulation of domains with geriatric deficits was a major significant factor for GHS/QOL decline at 3 months after treatment (per deficient domain beta = -3.17 [-5.04 to -1.30]) and deterioration during follow-up (per domain per year beta = -2.74 [-4.28 to -1.20]). Conclusions and Relevance In this prospective cohort study, geriatric deficits were significantly associated with HRQOL decline after treatment for HNC. Therefore, geriatric assessment may aid decision-making, indicate interventions, and reduce loss of HRQOL

A comparison of weekly versus 3-weekly cisplatin during adjuvant radiotherapy for high-risk head and neck cancer

SummaryObjectivesTo compare cumulative cisplatin dose and toxicity between patients who received 3-weekly versus weekly cisplatin during adjuvant radiotherapy for high-risk head and neck squamous cell carcinoma (HNSCC).Materials and methodsConsecutive HNSCC patients with involved resection margins and/or extra-capsular extension in two tertiary cancer centers with different institutional practices were identified. Cumulative cisplatin dose was calculated and information on toxicity reviewed and compared between patients who received 3-weekly versus weekly cisplatin.ResultsOf 270 high risk patients, 60 received 3-weekly 100mg/m2 and 48 received weekly 50mg/m2 cisplatin during adjuvant radiotherapy (60–66Gy in 30–33 fractions). Fourteen patients received other chemotherapy schedules and 148 received no chemotherapy. Mean cumulative cisplatin dose was 199.4mg/m2 (standard error (SE) 5.4) in 3-weekly versus 239.8mg/m2 (SE 11.0, P=0.001) in weekly treated patients. Cumulative cisplatin ⩾200mg/m2 was given to 67.7% of patients in the 3-weekly cohort and 85.2% (P=0.039) in the weekly cohort. The rate of feeding tube dependency 6months after treatment, osteoradionecrosis, neutropenic fever, and persistent renal function decline were not statistically different.ConclusionsAbout one half of high-risk HNSCC patients are not eligible for cisplatin during postoperative radiotherapy. Patients treated with weekly 50mg/m2 cisplatin received a higher cumulative dose with comparable toxicity as patients who received 3-weekly 100mg/m2 cisplatin. Efficacy and applicability to the frequently used weekly 40mg/m2 schedule remains to be evaluated

The effect of treatment delay on quality of life and overall survival in head and neck cancer patients

OBJECTIVE: Head and neck squamous cell carcinomas (HNSCC) are rapidly developing tumours, and substantial delay in treatment initiation is associated with decreased overall survival. The effect of delay on health-related quality of life (HRQOL) is unknown. The aim of this study was to assess the impact of delay on QOL and overall survival. METHODS: Patients with mucosal HNSCC were prospectively included. HRQOL and 2-year overall survival were analysed using linear mixed-model analyses and cox regression, respectively. Delay was defined as care pathway interval (CPI) of ≥30 days between first consultation and treatment initiation. RESULTS: Median CPI was 39 days for the 173 patients included. A trend towards higher HRQOL-scores (indicating better HRQOL) during 2-year follow-up for patients with delay in treatment initiation was visible in the adjusted models (HRQOL summary score-β: 2.62, 95% CI: 0.57-4.67, p = 0.012). Factors associated with decreased overall survival were moderate comorbidities (HR: 5.10, 95% CI: 1.65-15.76, p = 0.005) and stage-IV tumours (HR: 12.37, 95% CI: 2.81-54.39, p = 0.001). Delay was not associated with worse overall survival. CONCLUSION: Timely treatment initiation is challenging, especially for patients with advanced tumours and initial radiotherapy treatment. Encountering delay in treatment initiation did not result in clinically relevant differences in HRQOL-scores or decreased overall survival during 2-year follow-up

Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters

Purpose: To develop and validate prediction models of overall survival (OS) for head and neck cancer (HNC) patients based on image biomarkers (IBMs) of the primary tumor and positive lymph nodes (Ln) in combination with clinical parameters. Material and methods: The study cohort was composed of 289 nasopharyngeal cancer (NPC) patients from China and 298 HNC patients from the Netherlands. Multivariable Cox-regression analysis was performed to select clinical parameters from the NPC and HNC datasets, and IBMs from the NPC dataset. Final prediction models were based on both IBMs and clinical parameters. Results: Multivariable Cox-regression analysis identified three independent IBMs (tumor Volume density, Run Length Non-uniformity and Ln Major-axis-length). This IBM model showed a concordance (c)-index of 0.72 (95%Cl: 0.65-0.79) for the NPC dataset, which performed reasonably with a c-index of 0.67 (95%Cl: 0.62-0.72) in the external validation HNC dataset. When IBMs were added in clinical models, the c-index of the NPC and HNC datasets improved to 0.75 (95%Cl: 0.68-0.82; p = 0.019) and 0.75 (95%Cl: 0.70-0.81; p <0.001), respectively. Conclusion: The addition of IBMs from the primary tumor and Ln improved the prognostic performance of the models containing clinical factors only. These combined models may improve pre-treatment individualized prediction of OS for HNC patients. (C) 2017 The Authors. Published by Elsevier Ireland Ltd