Background Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose–volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient‐specific quality assurance (PSQA). However, it is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly. Purpose To develop a DVH‐based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH‐based PSQA. Methods A DL model with a three‐dimensional squeeze‐and‐excitation residual blocks incorporated into a modified U‐net was developed to predict the measured PSQA DVHs of 208 head‐and‐neck (H&N) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation, and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training. Results The prediction model was able to achieve a good agreement between predicted, measured, and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for Dmean of PTV6900 (p = 0.001), D50 of PTV6000 (p = 0.014), D2 of PTV5400 (p = 0.009), D50 of left parotid (p = 0.015), and Dmax of left inner ear (p = 0.007). The XGBoost model achieved an area under curves, accuracy, sensitivity, and specificity of 0.89 versus 0.88, 0.89 versus 0.86, 0. 71 versus 0.71, and 0.95 versus 0.91 with measured and predicted PSQA doses, respectively. The agreement between domain experts and the classification model was 86% for 28 test cases. Conclusions The successful prediction of PSQA doses and classification of PoF for H&N VMAT PSQA indicating that this DVH‐based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery.

Purpose: Although intensity-modulated radiotherapy (IMRT) is now a preferred option for conventionally fractionated RT in lung cancer, the commonly used cutoff values of the dosimetric constraints are still mainly derived from the data using three-dimensional conformal radiotherapy (3D-CRT). We aimed to compare the prediction performance among different dosimetric parameters for acute radiation pneumonitis (RP) in patients with lung cancer received IMRT. Methods: A total of 236 patients treated with IMRT were retrospectively reviewed in two independent groups of lung cancer from January 2014 to August 2018. The primary endpoint was grade 2 or higher acute RP (RP2). Dose metrics were generated from the bilateral lung volume outside GTV (Vdose G) and PTV (Vdose P). The associations of RP2 with clinical variables, dose-volume parameters and mean lung dose (MLD) were analyzed by univariate and multivariate logistic regression. The power of discrimination among each predictor was assessed by employing the bootstrapped area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and the integrated discrimination improvement (IDI). Results: Thirty-four (14.4%) out of 236 patients developed acute RP2 after the end of IMRT. The clinical parameters were identified as less important predictors for RP2 based on univariate and multivariate analysis. In both studied groups, the significance of association was more convincing in V20 P , V30 P , and MLD P (smaller Ps) than V5 G and V5 P. The largest bootstrapped AUC was identified for the V30 P. We found a trend of better

ObjectiveWe aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC.Methods170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Clinical features and enhanced CT images were collected and assessed. Quantitative CT analysis was performed based on feature types including first order, shape, gray-level co-occurrence matrix-based, gray-level size zone matrix-based, gray-level run length matrix-based, gray-level dependence matrix-based, neighboring gray tone difference matrix-based features and transform types including Log, wavelet and local binary pattern. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to value the ability to identify the lung IAC with MPP using these characteristics.ResultsUsing quantitative CT analysis, one thousand three hundred and seventeen radiomics features were deciphered from R (https://www.r-project.org/). Then these radiomic features were decreased to 14 features after dimension reduction using the least absolute shrinkage and selection operator (LASSO) method in R. After correlation analysis, 5 key features were obtained and used as signatures for predicting MPP within IAC. The individualized prediction model which included age, smoking, family tumor history and radiomics signature had better identification (AUC=0.739) in comparison with the model consisting only of radiomics features (AUC=0.722). DeLong test showed that the difference in AUC between the two models was statistically significant (P<0.01). Compared with the simple radiomics model, the more comprehensive individual prediction model has better prediction performance.ConclusionThe use of radiomics approach is of great value in the diagnosis of tumors by non-invasive means. The individualized prediction model in the study, when incorporated with age, smoking and radiomics signature, had effective predictive performance of lung IAC with MPP lesions. The combination of imaging features and clinical features can provide additional diagnostic value to identify the micropapillary pattern in IAC and can affect clinical diagnosis and treatment.

Background: To quantitatively evaluate lung damage after treatment of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) and stereotactic body radiotherapy (SBRT) in patients with nonsmall cell lung cancer (NSCLC), and compare that of SBRT only treatment.Methods: Eligible patients from an IRB-approved prospective clinical trial had one month of EGFR-TKIs treatment followed by SBRT (TKI + SBRT) and with 3-month follow-up high resolution CT. NSCLC patients treated with SBRT alone during the same time period without EGFR-TKIs or other systemic therapies were identified as controls. The lung damage was assessed clinically by pneumonitis and quantitatively using by CT intensity (Hounsfield unit, HU) changes. The mean HU values were extracted for regions of the lungs receiving the same dose range at 10 Gy intervals to generate dose-response curves (DRC). The relationship of HU changes and radiation dose was modeled using a Probit model.Results: Four out of 20 (25%) TKI + SBRT patients and none of 19 (0%) SBRT alone patients had developed grade 2 and above pneumonitis (P=0.053), respectively. Sixty percent of TKI + SBRT patients and 30% SBRT alone patients had HU changes of the normal lung density >200 HU, respectively. There were significant differences in the DRC and in lung HU changes between the two groups (all P<0.05). The physical dose for a 50% complication risk (TD 50 ) of CT lung damage was 52 Gy (CI: 46-59) in TKI + SBRT group versus 72 Gy (CI: 58-107) in SBRT alone group (P<0.01).Conclusions: Compared to patients treated with SBRT alone, patients treated with EGFR-TKIs followed by SBRT were more incline to develop radiation pneumonitis, and resulted in greater lung CT intensity changes and steeper dose-CT lung damage response relationship at 3 months post treatment. Future study with larger number of patients and longer follow-up period is warranted to validate this finding.

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Purpose: to develop a radiogenomic model on the basis of 18F-FDG PET/CT radiomics and clinical-parameter EGFR for predicting PFS stratification in lung-cancer patients after SBRT treatment. Methods: A total of 123 patients with lung cancer who had undergone 18F-FDG PET/CT examination before SBRT from September 2014 to December 2021 were retrospectively analyzed. All patients’ PET/CT images were manually segmented, and the radiomic features were extracted. LASSO regression was used to select radiomic features. Logistic regression analysis was used to screen clinical features to establish the clinical EGFR model, and a radiogenomic model was constructed by combining radiomics and clinical EGFR. We used the receiver operating characteristic curve and calibration curve to assess the efficacy of the models. The decision curve and influence curve analysis were used to evaluate the clinical value of the models. The bootstrap method was used to validate the radiogenomic model, and the mean AUC was calculated to assess the model. Results: A total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849–0.851). Conclusions: The radiogenomic model based on 18F-FDG PET/CT radiomics and clinical EGFR has good application value in predicting the PFS stratification of lung-cancer patients after SBRT treatment.

proportional hazards model against the numbers of resected TLNs and MLNs were depicted and curves were fit using a LOWESS smoother. Cutoff points for the optimal numbers of resected lymph nodes were further determined by Chow test. KaplaneMeier method was used to compare the overall survival (OS) between groups divided by the cutoff points. Result: A total of 2,444 patients were included in this study and adenocarcinoma accounted for most of the cases (adenocarcinoma: 1,522/2,444, 62.3%; squamous-cell carcinoma: 784/2,444, 32.1%; others: 138/2,444, 5.6%). Mean numbers of resected TLNs and MLNs were 19.4 ± 11.0 (median: 17) and 12.1 ± 8.6 (median: 10). Cox regression analysis suggested that the increasing numbers of resected TLNs/MLNs were independent factors favoring OS in adenocarcinoma (TLNs: HR ¼ 0.983, 95% confidence interval [95% CI] 0.971 to 0.996, P <0.01; MLNs: HR ¼ 0.983, 95% CI 0.968 to 0.999, P ¼ 0.034). Curves of HRs against resected numbers of TLNs/MLNs with Chow test suggested that 17 resected TLNs and 12 resected MLNs were optimal cutoff points for prolonged OS in adenocarcinoma. Furthermore, both the cutoff points were confirmed by OS comparison (5-year OS: 84.2% [!17 TLNs] vs. 77.9% [<17 TLNs], P ¼ 0.02; 84.4% [!12 MLNs] vs. 78.9% [<12 MLNs], P ¼ 0.04) and Cox regression model (TLNs: univariate HR ¼ 0.754, 95% CI 0.593 to 0.959, P ¼ 0.021, multivariate HR ¼ 0.712, 95% CI 0.556 to 0.914, P <0.01; MLNs: univariate HR ¼ 0.769, 95% CI 0.598 to 0.988, P ¼ 0.040, multivariate HR ¼ 0.730, 95% CI 0.560 to 0.952, P ¼ 0.020). However, the numbers of resected TLNs/MLNs were not associated with OS in non-adenocarcinoma (TLNs: HR ¼ 0.998, 95% CI 0.987 to 1.009, P ¼ 0.756; MLNs: HR ¼ 1.000, 95% CI 0.986 to 1.015, P ¼ 0.988). Conclusion: The number of resected lymph nodes associated with OS in N0 lung adenocarcinoma patients. At least 17 TLNs and 12 MLNs are required to be resected to warrant the longterm survival in these patients.