Dataset Information

A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

ABSTRACT: PURPOSE:Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensional (3D) residual convolutional neural network (rCNN). The model can be used to guide radiation therapy to reduce toxicity. METHODS AND MATERIALS:A total of 784 patients with head and neck squamous cell carcinoma enrolled in the Radiation Therapy Oncology Group 0522 clinical trial were included in this study. Late xerostomia is defined as xerostomia of grade ?2 occurring in the 12th month of radiation therapy. The computed tomography (CT) planning images, 3D dose distributions, and contours of the parotid and submandibular glands were included as 3D rCNN inputs. Comparative experiments were performed for the 3D rCNN model without 1 of the 3 inputs and for the logistic regression model. Accuracy, sensitivity, specificity, F-score, and area under the receiver operator characteristic curve were evaluated. RESULTS:The proposed model achieved promising prediction results. The performance metrics for 3D rCNN model with contour, CT images, and radiation therapy dose; 3D rCNN without contour; 3D rCNN without CT images; 3D rCNN without the dose; logistic regression with the dose and clinical parameters; and logistic regression without clinical parameters were as follows: accuracy: 0.76, 0.74, 0.73, 0.65, 0.64, and 0.56; sensitivity: 0.76, 0.72, 0.77, 0.59, 0.72, and 0.75; specificity: 0.76, 0.76, 0.71, 0.69, 0.59, and 0.43; F-score: 0.70, 0.68, 0.69, 0.56, 0.60, and 0.57; and area under the receiver operator characteristic curve: 0.84, 0.82, 0.78, 0.70, 0.74, and 0.68, respectively. CONCLUSIONS:The proposed model uses 3D rCNN filters to extract low- and high-level spatial features and to achieve promising performance. This is a potentially effective model for predicting objective toxicity for supporting clinical decision making.

SUBMITTER: Men K

PROVIDER: S-EPMC6732004 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

Men Kuo K Geng Huaizhi H Zhong Haoyu H Fan Yong Y Lin Alexander A Xiao Ying Y

International journal of radiation oncology, biology, physics 20190613 2

<h4>Purpose</h4>Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensional (3D) residual convolutional neural network (rCNN). The model can be used to guide radiation therapy to reduce toxicity.<h4>Methods and materials</h4>A total of 784 patients with head and neck squamous cell carcinoma enrolled in the Radiati ...[more]

PMID: 31201897

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Project description:PurposeCombining cisplatin or cetuximab with radiation improves overall survival (OS) of patients with stage III or IV head and neck carcinoma (HNC). Cetuximab plus platinum regimens also increase OS in metastatic HNC. The Radiation Therapy Oncology Group launched a phase III trial to test the hypothesis that adding cetuximab to the radiation-cisplatin platform improves progression-free survival (PFS).Patients and methodsEligible patients with stage III or IV HNC were randomly assigned to receive radiation and cisplatin without (arm A) or with (arm B) cetuximab. Acute and late reactions were scored using Common Terminology Criteria for Adverse Events (version 3). Outcomes were correlated with patient and tumor features and markers.ResultsOf 891 analyzed patients, 630 were alive at analysis (median follow-up, 3.8 years). Cetuximab plus cisplatin-radiation, versus cisplatin-radiation alone, resulted in more frequent interruptions in radiation therapy (26.9% v. 15.1%, respectively); similar cisplatin delivery (mean, 185.7 mg/m2 v. 191.1 mg/m2, respectively); and more grade 3 to 4 radiation mucositis (43.2% v. 33.3%, respectively), rash, fatigue, anorexia, and hypokalemia, but not more late toxicity. No differences were found between arms A and B in 30-day mortality (1.8% v. 2.0%, respectively; P = .81), 3-year PFS (61.2% v. 58.9%, respectively; P = .76), 3-year OS (72.9% v. 75.8%, respectively; P = .32), locoregional failure (19.9% v. 25.9%, respectively; P = .97), or distant metastasis (13.0% v. 9.7%, respectively; P = .08). Patients with p16-positive oropharyngeal carcinoma (OPC), compared with patients with p16-negative OPC, had better 3-year probability of PFS (72.8% v. 49.2%, respectively; P < .001) and OS (85.6% v. 60.1%, respectively; P < .001), but tumor epidermal growth factor receptor (EGFR) expression did not distinguish outcome.ConclusionAdding cetuximab to radiation-cisplatin did not improve outcome and hence should not be prescribed routinely. PFS and OS were higher in patients with p16-positive OPC, but outcomes did not differ by EGFR expression.

Project description:PurposeTo analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy.MethodsA retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p?<?0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n?=?50) of patients who were treated at the same institution in 2017-2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT?+?MR, DVH?+?CT, DVH?+?CT?+?MR, Clinical+CT?+?MR, and Clinical+DVH?+?CT?+?MR) using the area under the receiver operating characteristic curve (ROC-AUC).ResultsAmong extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n?=?216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH?+?CT?+?MR features were 0.73?±?0.01, 0.69?±?0.01, 0.70?±?0.01, and 0.79?±?0.01, respectively. The validation cohort (n?=?50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH?+?CT?+?MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p?=?0.8) or MR-ROC (p?=?0.4). However, the CT?+?MR-ROC was significantly different than the CT-ROC (p?=?0.03), but not the Clinical+DVH?+?CT?+?MR model (p?=?0.5).ConclusionOur results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy.

Project description:PURPOSE:To report results of a randomized phase II trial (Radiation Therapy Oncology Group RTOG-0234) examining concurrent chemoradiotherapy and cetuximab in the postoperative treatment of patients with squamous cell carcinoma of the head and neck (SCCHN) with high-risk pathologic features. PATIENTS AND METHODS:Eligibility required pathologic stage III to IV SCCHN with gross total resection showing positive margins and/or extracapsular nodal extension and/or two or more nodal metastases. Patients were randomly assigned to 60 Gy radiation with cetuximab once per week plus either cisplatin 30 mg/m(2) or docetaxel 15 mg/m(2) once per week. RESULTS:Between April 2004 and December 2006, 238 patients were enrolled. With a median follow-up of 4.4 years, 2-year overall survival (OS) was 69% for the cisplatin arm and 79% for the docetaxel arm; 2-year disease-free survival (DFS) was 57% and 66%, respectively. Patients with p16-positive oropharynx tumors showed markedly improved survival outcome relative to patients with p16-negative oropharynx tumors. Grade 3 to 4 myelosuppression was observed in 28% of patients in the cisplatin arm and 14% in the docetaxel arm; mucositis was observed in 56% and 54%, respectively. DFS in this study was compared with that in the chemoradiotherapy arm of the RTOG-9501 trial (Phase III Intergroup Trial of Surgery Followed by Radiotherapy Versus Radiochemotherapy for Resectable High Risk Squamous Cell Carcinoma of the Head and Neck), which had a hazard ratio of 0.76 for the cisplatin arm versus control (P = .05) and 0.69 for the docetaxel arm versus control (P = .01), reflecting absolute improvement in 2-year DFS of 2.5% and 11.1%, respectively. CONCLUSION:The delivery of postoperative chemoradiotherapy and cetuximab to patients with SCCHN is feasible and tolerated with predictable toxicity. The docetaxel regimen shows favorable outcome with improved DFS and OS relative to historical controls and has commenced formal testing in a phase II/III trial.

Project description:PurposeTo determine whether serial cone beam computed tomography (CBCT) images taken during head and neck radiation therapy (HNR) can improve chronic xerostomia prediction.Methods and materialsIn a retrospective analysis, parotid glands (PGs) were delineated on daily kV CBCT images using deformable image registration for 119 HNR patients (60 or 70 Gy in 2 Gy fractions over 6 or 7 weeks). Deformable image registration accuracy for a subset of deformed contours was quantified using the Dice similarity coefficient and mean distance to agreement in comparison with manually drawn contours. Average weekly changes in CBCT-measured mean Hounsfield unit intensity and volume were calculated for each PG relative to week 1. Dose-volume histogram statistics were extracted from each plan, and interactions among dose, volume, and intensity were investigated. Univariable analysis and penalized logistic regression were used to analyze association with observer-rated xerostomia at 1 year after HNR. Models including CBCT delta imaging features were compared with clinical and dose-volume histogram-only models using area under the receiver operating characteristic curve (AUC) for grade ≥1 and grade ≥2 xerostomia prediction.ResultsAll patients experienced end-treatment PG volume reduction with mean (range) ipsilateral and contralateral PG shrinkage of 19.6% (0.9%-58.4%) and 17.7% (4.4%-56.3%), respectively. Midtreatment volume change was highly correlated with mean PG dose (r = -0.318, P < 1e-6). Incidence of grade ≥1 and grade ≥2 xerostomia was 65% and 16%, respectively. For grade ≥1 xerostomia prediction, the delta-imaging model had an AUC of 0.719 (95% confidence interval [CI], 0.603-0.830), compared with 0.709 (95% CI, 0.603-0.815) for the dose/clinical model. For grade ≥2 xerostomia prediction, the dose/clinical model had an AUC of 0.692 (95% CI, 0.615-0.770), and the addition of contralateral PG changes modestly improved predictive performance, with an AUC of 0.776 (0.643-0.912).ConclusionsThe rate of CBCT-measured PG image feature changes improves prediction over dose alone for chronic xerostomia prediction. Analysis of CBCT images acquired for treatment positioning may provide an inexpensive monitoring system to support toxicity-reducing adaptive radiation therapy.

Project description:For TP53-mutated head and neck squamous cell carcinomas (HNSCCs), the codon and specific amino acid sequence change resulting from a patient's mutation can be prognostic. Thus, developing a framework to predict patient survival for specific mutations in TP53 would be valuable. There are many bioinformatics and functional methods for predicting the phenotypic impact of genetic variation, but their overall clinical value remains unclear. Here, we assess the ability of 15 different methods to predict HNSCC patient survival from TP53 mutation, using TP53 mutation and clinical data from patients enrolled in E4393 by the Eastern Cooperative Oncology Group (ECOG), which investigated whether TP53 mutations in surgical margins were predictive of disease recurrence. These methods include: server-based computational tools SIFT, PolyPhen-2, and Align-GVGD; our in-house POSE and VEST algorithms; the rules devised in Poeta et al. with and without considerations for splice-site mutations; location of mutation in the DNA-bound TP53 protein structure; and a functional assay measuring WAF1 transactivation in TP53-mutated yeast. We assessed method performance using overall survival (OS) and progression-free survival (PFS) from 420 HNSCC patients, of whom 224 had TP53 mutations. Each mutation was categorized as "disruptive" or "non-disruptive". For each method, we compared the outcome between the disruptive group vs. the non-disruptive group. The rules devised by Poeta et al. with or without our splice-site modification were observed to be superior to others. While the differences in OS (disruptive vs. non-disruptive) appear to be marginally significant (Poeta rules + splice rules, P = 0.089; Poeta rules, P = 0.053), both algorithms identified the disruptive group as having significantly worse PFS outcome (Poeta rules + splice rules, P = 0.011; Poeta rules, P = 0.027). In general, prognostic performance was low among assessed methods. Further studies are required to develop and validate methods that can predict functional and clinical significance of TP53 mutations in HNSCC patients.

Project description:PurposeRadiation-induced xerostomia is one of the most prevalent symptoms during and after head and neck cancer radiation therapy (RT). We aimed to discover the spatial radiation dose-based (voxel dose) importance pattern in the major salivary glands in relation to the recovery of xerostomia 18 months after RT, and to compare the recovery voxel dose importance pattern to the acute incidence (injury) pattern.Methods and materialsThis study included all patients within our database with xerostomia outcomes after completion of curative intensity modulated RT. Common Terminology Criteria for Adverse Events xerostomia grade was used to define recovered versus nonrecovered group at baseline, between end of treatment and 18 months post-RT, and beyond 18 months, respectively. Ridge logistic regression was performed to predict the probability of xerostomia recovery. Voxel doses within geometrically defined parotid glands (PG) and submandibular glands (SMG), demographic characteristics, and clinical factors were included in the algorithm. We plotted the normalized learned weights on the 3-dimensional PG and SMG structures to visualize the voxel dose importance for predicting xerostomia recovery.ResultsA total of 146 head and neck cancer patients from 2008 to 2016 were identified. The superior region of the ipsilateral and contralateral PG was the most influencial for xerostomia recovery. The area under the receiver operating characteristic curve evaluated using 10-fold cross-validation for ridge logistic regression was 0.68 ± 0.07. Compared with injury, the recovery voxel dose importance pattern was more symmetrical and was influenced by lower dose voxels.ConclusionsThe superior portion of the 2 PGs (low dose region) are the most influential on xerostomia recovery and seem to be equal in their contribution. The dissimilarity of the influence pattern between injury and recovery suggests different underlying mechanisms. The importance pattern identified by spatial radiation dose and machine learning methods can improve our understanding of normal tissue toxicities in RT. Further external validation is warranted.

Dataset Information

A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

Publications

A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

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