Dataset Information

Deep Learning Improved Clinical Target Volume Contouring Quality and Efficiency for Postoperative Radiation Therapy in Non-small Cell Lung Cancer.

ABSTRACT: Purpose: To investigate whether a deep learning-assisted contour (DLAC) could provide greater accuracy, inter-observer consistency, and efficiency compared with a manual contour (MC) of the clinical target volume (CTV) for non-small cell lung cancer (NSCLC) receiving postoperative radiotherapy (PORT). Materials and Methods: A deep dilated residual network was used to achieve the effective automatic contour of the CTV. Eleven junior physicians contoured CTVs on 19 patients by using both MC and DLAC methods independently. Compared with the ground truth, the accuracy of the contour was evaluated by using the Dice coefficient and mean distance to agreement (MDTA). The coefficient of variation (CV) and standard distance deviation (SDD) were rendered to measure the inter-observer variability or consistency. The time consumed for each of the two contouring methods was also compared. Results: A total of 418 CTV sets were generated. DLAC improved contour accuracy when compared with MC and was associated with a larger Dice coefficient (mean ± SD: 0.75 ± 0.06 vs. 0.72 ± 0.07, p < 0.001) and smaller MDTA (mean ± SD: 2.97 ± 0.91 mm vs. 3.07 ± 0.98 mm, p < 0.001). The DLAC was also associated with decreased inter-observer variability, with a smaller CV (mean ± SD: 0.129 ± 0.040 vs. 0.183 ± 0.043, p < 0.001) and SDD (mean ± SD: 0.47 ± 0.22 mm vs. 0.72 ± 0.41 mm, p < 0.001). In addition, a value of 35% of time saving was provided by the DLAC (median: 14.81 min vs. 9.59 min, p < 0.001). Conclusions: Compared with MC, the DLAC is a promising strategy to obtain superior accuracy, consistency, and efficiency for the PORT-CTV in NSCLC.

SUBMITTER: Bi N

PROVIDER: S-EPMC6863957 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Deep Learning Improved Clinical Target Volume Contouring Quality and Efficiency for Postoperative Radiation Therapy in Non-small Cell Lung Cancer.

Bi Nan N Wang Jingbo J Zhang Tao T Chen Xinyuan X Xia Wenlong W Miao Junjie J Xu Kunpeng K Wu Linfang L Fan Quanrong Q Wang Luhua L Li Yexiong Y Zhou Zongmei Z Dai Jianrong J

Frontiers in oncology 20191113

<b>Purpose:</b> To investigate whether a deep learning-assisted contour (DLAC) could provide greater accuracy, inter-observer consistency, and efficiency compared with a manual contour (MC) of the clinical target volume (CTV) for non-small cell lung cancer (NSCLC) receiving postoperative radiotherapy (PORT). <b>Materials and Methods:</b> A deep dilated residual network was used to achieve the effective automatic contour of the CTV. Eleven junior physicians contoured CTVs on 19 patients by using ...[more]

PMID: 31799181

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Project description:Purpose: Ensuring high-quality data for clinical trials in radiotherapy requires the generation of contours that comply with protocol definitions. The current workflow includes a manual review of the submitted contours, which is time-consuming and subjective. In this study, we developed an automated quality assurance (QA) system for lung cancer based on a segmentation model trained with deep active learning. Methods: The data included a gold atlas with 36 cases and 110 cases from the "NRG Oncology/RTOG 1308 Trial". The first 70 cases enrolled to the RTOG 1308 formed the candidate set, and the remaining 40 cases were randomly assigned to validation and test sets (each with 20 cases). The organs-at-risk included the heart, esophagus, spinal cord, and lungs. A preliminary convolutional neural network segmentation model was trained with the gold standard atlas. To address the deficiency of the limited training data, we selected quality images from the candidate set to be added to the training set for fine-tuning of the model with deep active learning. The trained robust segmentation models were used for QA purposes. The segmentation evaluation metrics derived from the validation set, including the Dice and Hausdorff distance, were used to develop the criteria for QA decision making. The performance of the strategy was assessed using the test set. Results: The QA method achieved promising contouring error detection, with the following metrics for the heart, esophagus, spinal cord, left lung, and right lung: balanced accuracy, 0.96, 0.95, 0.96, 0.97, and 0.97, respectively; sensitivity, 0.95, 0.98, 0.96, 1.0, and 1.0, respectively; specificity, 0.98, 0.92, 0.97, 0.94, and 0.94, respectively; and area under the receiving operator characteristic curve, 0.96, 0.95, 0.96, 0.97, and 0.94, respectively. Conclusions: The proposed system automatically detected contour errors for QA. It could provide consistent and objective evaluations with much reduced investigator intervention in multicenter clinical trials.

Project description:BackgroundAccurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or without intraventricular hemorrhage (IVH) extension. We compared this automated pipeline with two manual segmentation techniques.MethodsWe retrospectively reviewed 105 patients with acute spontaneous ICH. Depending on the presence of IVH extension, patients were divided into two groups: ICH without (n = 56) and with IVH (n = 49). ICH volume of the two groups were segmented and measured using a deep learning-based artificial intelligence (AI) diagnostic system and computed tomography-based planimetry (CTP), and the ABC/2 score were used to measure hemorrhage volume in the ICH without IVH group. Correlations and agreement analyses were used to analyze the differences in volume and length of processing time among the three segmentation approaches.ResultsIn the ICH without IVH group, the ICH volumes measured using AI and the ABC/2 score were comparable to CTP segmentation. Strong correlations were observed among the three segmentation methods (r = 0.994, 0.976, 0.974; P < 0.001; concordance correlation coefficient [CCC] = 0.993, 0.968, 0.967). But the absolute error of the ICH volume measured by the ABC/2 score was greater than that of the algorithm (P < 0.05). In the ICH with IVH group, there is no significant differences were found between algorithm and CTP(P = 0.614). The correlation and agreement between CTP and AI were strong (r = 0.996, P < 0.001; CCC = 0.996). The AI segmentation took a significantly shorter amount of time than CTP (P < 0.001), but was slightly longer than ABC/2 score technique (P = 0.002).ConclusionsThe deep learning-based AI diagnostic system accurately quantified volumes of acute spontaneous ICH with high fidelity and greater efficiency compared to the CTP measurement and more accurately than the ABC/2 scores. We believe this is a promising tool to help physicians achieve precise ICH quantification in practice.

Dataset Information

Deep Learning Improved Clinical Target Volume Contouring Quality and Efficiency for Postoperative Radiation Therapy in Non-small Cell Lung Cancer.

Publications

Deep Learning Improved Clinical Target Volume Contouring Quality and Efficiency for Postoperative Radiation Therapy in Non-small Cell Lung Cancer.

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