Effect on Heart and Lung Doses Reduction of Abdominal and Thoracic Deep Inspiratory Breath-hold Assuming Involved-field Radiation Therapy in Patients with Simulated Esophageal Cancer.

PURPOSE: The purpose of this study was to evaluate the lung and heart doses in volumetric-modulated arc therapy (VMAT) using involved-field irradiation in patients with middle-to-lower thoracic esophageal cancer during free breathing (FB), abdominal deep inspiratory breath-hold (A-DIBH), and thoracic DIBH (T-DIBH) images. METHODS: Computed tomography images of A-DIBH, T-DIBH, and FB from 25 patients with breast cancer were used to simulate patients with esophageal cancer. The irradiation field was set at an involved-field, and target and risk organs were outlined according to uniform criteria. VMAT optimization was performed, and lung and heart doses were evaluated. RESULTS: A-DIBH had a lower lung V20 Gy than FB and a lower lung V40 Gy, V30 Gy, V20 Gy than T-DIBH. The heart all dose indices were lower in T-DIBH than FB, and the heart V10 Gy was lower in A-DIBH than FB. However, the heart Dmean was comparable with A-DIBH and T-DIBH. CONCLUSIONS: A-DIBH had significant dose advantages for lungs compared to FB and T-DIBH, and the heart Dmean was comparable to T-DIBH. Therefore, when performing DIBH, A-DIBH is suggested for radiotherapy in patients with middle-to-lower thoracic esophageal cancer, excluding irradiation of the prophylactic area.

Relationship between Dose Prescription Methods and Local Control Rate in Stereotactic Body Radiotherapy for Early Stage Non-Small-Cell Lung Cancer: Systematic Review and Meta-Analysis.

Variations in dose prescription methods in stereotactic body radiotherapy (SBRT) for early stage non-small-cell lung cancer (ES-NSCLC) make it difficult to properly compare the outcomes of published studies. We conducted a comprehensive search of the published literature to summarize the outcomes by discerning the relationship between local control (LC) and dose prescription sites. We systematically searched PubMed to identify observational studies reporting LC after SBRT for peripheral ES-NSCLC. The correlations between LC and four types of biologically effective doses (BED) were evaluated, which were calculated from nominal, central, and peripheral prescription points and, from those, the average BED. To evaluate information on SBRT for peripheral ES-NSCLC, 188 studies were analyzed. The number of relevant articles increased over time. The use of an inhomogeneity correction was mentioned in less than half of the articles, even among the most recent. To evaluate the relationship between the four BEDs and LC, 33 studies were analyzed. Univariate meta-regression revealed that only the central BED significantly correlated with the 3-year LC of SBRT for ES-NSCLC (p = 0.03). As a limitation, tumor volume, which might affect the results of this study, could not be considered due to a lack of data. In conclusion, the central dose prescription is appropriate for evaluating the correlation between the dose and LC of SBRT for ES-NSCLC. The standardization of SBRT dose prescriptions is desirable.

Applying Artificial Neural Networks to Develop a Decision Support Tool for Tis-4N0M0 Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiotherapy.

PURPOSE: Clear evidence indicating whether surgery or stereotactic body radiation therapy (SBRT) is best for non-small-cell lung cancer (NSCLC) is lacking. SBRT has many advantages. We used artificial neural networks (NNs) to predict treatment outcomes for patients with NSCLC receiving SBRT, aiming to aid in decision making. PATIENTS AND METHODS: Among consecutive patients receiving SBRT between 2005 and 2019 in our institution, we retrospectively identified those with Tis-T4N0M0 NSCLC. We constructed two NNs for prediction of overall survival (OS) and cancer progression in the first 5 years after SBRT, which were tested using an internal and an external test data set. We performed risk group stratification, wherein 5-year OS and cancer progression were stratified into three groups. RESULTS: In total, 692 patients in our institution and 100 patients randomly chosen in the external institution were enrolled. The NNs resulted in concordance indexes for OS of 0.76 (95% CI, 0.73 to 0.79), 0.68 (95% CI, 0.60 to 0.75), and 0.69 (95% CI, 0.61 to 0.76) and area under the curve for cancer progression of 0.80 (95% CI, 0.75 to 0.84), 0.72 (95% CI, 0.60 to 0.83), and 0.70 (95% CI, 0.57 to 0.81) in the training, internal test, and external test data sets, respectively. The survival and cumulative incidence curves were significantly stratified. NNs selected low-risk cancer progression groups of 5.6%, 6.9%, and 7.0% in the training, internal test, and external test data sets, respectively, suggesting that 48% of patients with peripheral Tis-4N0M0 NSCLC can be at low-risk for cancer progression. CONCLUSION: Predictions of SBRT outcomes using NNs were useful for Tis-4N0M0 NSCLC. Our results are anticipated to open new avenues for NN predictions and provide decision-making guidance for patients and physicians.

Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs.

Deep learning has demonstrated high efficacy for automatic segmentation in contour delineation, which is crucial in radiation therapy planning. However, the collection, labeling, and management of medical imaging data can be challenging. This study aims to elucidate the effects of sample size and data augmentation on the automatic segmentation of computed tomography images using U-Net, a deep learning method. For the chest and pelvic regions, 232 and 556 cases are evaluated, respectively. We investigate multiple conditions by changing the sum of the training and validation datasets across a broad range of values: 10-200 and 10-500 cases for the chest and pelvic regions, respectively. A U-Net is constructed, and horizontal-flip data augmentation, which produces left and right inverse images resulting in twice the number of images, is compared with no augmentation for each training session. All lung cases and more than 100 prostate, bladder, and rectum cases indicate that adding horizontal-flip data augmentation is almost as effective as doubling the number of cases. The slope of the Dice similarity coefficient (DSC) in all organs decreases rapidly until approximately 100 cases, stabilizes after 200 cases, and shows minimal changes as the number of cases is increased further. The DSCs stabilize at a smaller sample size with the incorporation of data augmentation in all organs except the heart. This finding is applicable to the automation of radiation therapy for rare cancers, where large datasets may be difficult to obtain.

Repeated Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma.

PURPOSE: In clinical practice, whether cirrhotic livers in patients with hepatocellular carcinoma (HCC) can withstand repeated stereotactic body radiation therapy (SBRT) remains unclear. This study aimed to evaluate the outcomes and toxicities in these patients. METHODS AND MATERIALS: This retrospective study included patients with HCC who were treated with SBRT at least twice between January 2012 and June 2019. Local control and overall survival rates were calculated. Liver function before and after irradiation was evaluated using the Child-Pugh score and modified albumin-bilirubin grade. All toxicities were assessed using the Common Terminology Criteria for Adverse Events (version 4.0). RESULTS: Fifty-two patients underwent 136 courses (148 lesions) of SBRT, which was mostly performed for out-of-field tumors but 3 in-field recurrences. The median follow-up duration from the first SBRT was 52.6 months (range, 15.7-89.3 months). The median gross tumor volume was 4.6 cm3 (range, 0.8-55.2 cm3) at the second SBRT. The 3-year local control rate was 94.5% (95% confidence interval, 88.0%-97.5%). The 3-year overall survival rate after the second course was 62.8% (95% confidence interval, 45.1%-76.2%). Although the Child-Pugh score did not deteriorate after the second course, deterioration of the modified albumin-bilirubin grade at 6, 12, and 24 months was statistically significant compared with that before the second course. One patient (1.9%) experienced grade 3 hypoalbuminemia and 2 patients (3.8%) had grade 3 thrombocytopenia 6 months after the second course. Mild fatigue and nausea were reported in 9 (17.3%) and 6 (11.5%) patients, respectively. One instance of grade 5 toxicity was observed. Two patients (1.5%) had grade 2 gastric ulcers. No other grade ≥3 gastrointestinal toxicities occurred. CONCLUSIONS: Repeated SBRT is feasible and produces minimal toxicity in patients with HCC and Child-Pugh scores of ≤7 and a low normal liver dose.

Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images.

PURPOSE: Deep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management of medical imaging data, because of ethical complications and the limited number of imaging studies available at a single facility. This study aimed to find a simple and low-cost method to increase the accuracy of deep learning semantic segmentation for radiation therapy of prostate cancer. METHODS: In total, 556 cases with non-contrast CT images for prostate cancer radiation therapy were examined using a two-dimensional U-Net. Initially, all slices were used for the input data. Then, we removed slices of the cranial portions, which were beyond the margins of the bladder and rectum. Finally, the ground truth labels for the bladder and rectum were added as channels to the input for the prostate training dataset. RESULTS: The highest mean dice similarity coefficients (DSCs) for each organ in the test dataset of 56 cases were 0.85 ± 0.05, 0.94 ± 0.04 and 0.85 ± 0.07 for the prostate, bladder and rectum, respectively. Removal of the cranial slices from the original images significantly increased the DSC of the rectum from 0.83 ± 0.09 to 0.85 ± 0.07 (p < 0.05). Adding bladder and rectum information to prostate training without removing the slices significantly increased the DSC of the prostate from 0.79 ± 0.05 to 0.85 ± 0.05 (p < 0.05). CONCLUSIONS: These cost-free approaches may be useful for new applications, which may include updated models and datasets. They may be applicable to other organs at risk (OARs) and clinical targets such as elective nodal irradiation.

Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi.

This study aimed to examine the efficacy of semantic segmentation implemented by deep learning and to confirm whether this method is more effective than a commercially dominant auto-segmentation tool with regards to delineating normal lung excluding the trachea and main bronchi. A total of 232 non-small-cell lung cancer cases were examined. The computed tomography (CT) images of these cases were converted from Digital Imaging and Communications in Medicine (DICOM) Radiation Therapy (RT) formats to arrays of 32 × 128 × 128 voxels and input into both 2D and 3D U-Net, which are deep learning networks for semantic segmentation. The number of training, validation and test sets were 160, 40 and 32, respectively. Dice similarity coefficients (DSCs) of the test set were evaluated employing Smart SegmentationⓇ Knowledge Based Contouring (Smart segmentation is an atlas-based segmentation tool), as well as the 2D and 3D U-Net. The mean DSCs of the test set were 0.964 [95% confidence interval (CI), 0.960-0.968], 0.990 (95% CI, 0.989-0.992) and 0.990 (95% CI, 0.989-0.991) with Smart segmentation, 2D and 3D U-Net, respectively. Compared with Smart segmentation, both U-Nets presented significantly higher DSCs by the Wilcoxon signed-rank test (P < 0.01). There was no difference in mean DSC between the 2D and 3D U-Net systems. The newly-devised 2D and 3D U-Net approaches were found to be more effective than a commercial auto-segmentation tool. Even the relatively shallow 2D U-Net which does not require high-performance computational resources was effective enough for the lung segmentation. Semantic segmentation using deep learning was useful in radiation treatment planning for lung cancers.