CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy.

Purpose: To establish a radiomics model using radiomics features from different region of interests (ROI) based on dosimetry-related regions in enhanced computed tomography (CT) simulated images to predict radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC). Methods: Our retrospective study was conducted based on a cohort of 236 NSCLC patients (59 of them with RP≥2) who were treated in 2 institutions and divided into the primary cohort (n = 182,46 of them with RP≥2) and external validation cohort (n = 54,13 of them with RP≥2). Radiomic features extracted from three ROIs were defined as the whole lung (WL), the dose volume histogram (DVH) of the lung V20 (V20_Lung) and the DVH of the V30 of lung minus the planning target volume (PTV) (V30 Lung-PTV). A total of 107 radiomics features were extracted from each ROIs. The U test, correlation coefficient and least absolute shrinkage and selection operator (LASSO) were performed for features selection. Six models based on different classification algorithms were developed to select the best radiomics model (R model).In addition, we built a dosimetry model then combined it with the best R model to create a mixed model (R+D model) The receiver operating characteristic (ROC) curve was delineated to assess the predictive efficacy of the models. Decision curve analysis could benefit from the model proposals through the assessment of clinical utility. Results: Among the three ROIs, the best R model constructed from the LightGBM algorithm demonstrated the strongest discriminative ability in the ROI of V30 Lung-PTV. The corresponding area under the curve (AUC) value was 0.930 (95 % confidence interval (CI): 0.829-0.941). The D model, R model and R+D model achieved AUC values of 0.798 (95 %CI: 0.732-0.865), 0.930 (95 %CI: 0.829-0.941) and 0.940 (95 %CI: 0.906-0.974) in primary cohort, and in external validation cohort, the AUC values were 0.793 (95 %CI:0.637-0.949), 0.887 (95 %CI:0.810-0.993), 0.951 (95CI%:0.891-1.000). Decision curve demonstrate that R+D model could benefit for patients through the assessment of clinical utility. Conclusion: The radiomics model was able to predict the acute RP more effectively in comparison with the traditional dosimetry model. Especially the radiomics model based on the V30 Lung-PTV region was able to achieve a higher accuracy when compared to the other regions.

Current state, challenges, and future perspective of adaptive radiotherapy: A narrative review of nasopharyngeal carcinoma.

Patients with nasopharyngeal carcinoma often experience weight loss and tumor regression during the course of radiotherapy that lasts for up to 6-7 weeks. Adaptive radiotherapy is a systematic feedback control approach based on image-guided technology that adjusts these changes and optimizes the radiotherapy plans according to new imaging findings during treatment. There is growing evidence that adaptive radiotherapy can reduce side effects, improve the quality of life, and enhance disease control. However, the routine application of adaptive radiotherapy for nasopharyngeal remains relatively limited. This review discusses the necessity, clinical benefits, and limitations of adaptive radiotherapy, and presents the current state, challenges, and future perspective of adaptive radiotherapy strategies for nasopharyngeal carcinoma.

Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy.

BACKGROUND: Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied. PURPOSE: This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity-modulated radiation therapy (IMRT) treatment planning. METHODS: A double-UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high-dose region-controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O-P)] and [body avoids OARs & PTV(B-OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two-step process. In the beginning, the plans of two extra cases with and without low-dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O-P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O-P FEs from first step were constrained again but weakly and the dose of the B-OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double-UNet dose prediction model trained by low and high region-controlled plans (method 2). RESULTS: The mean differences in dose metrics between the UNet-predicted dose and the clinical plans were: 0.47 Gy for bladder D50% ; 0.62 Gy for rectum D50% ; 0% for small intestine V30Gy ; 1% for small intestine V40Gy ; 4% for left femoral head V30Gy ; and 6% for right femoral head V30Gy . The reductions in mean dose (p < 0.001) after FE-based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7 Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7 Gy), rectum (1.0 Gy), and small intestine (0.6 Gy), while maintaining  PTV homogeneity and conformity. CONCLUSION: FE-based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low-dose regions.

CT-based radiomics nomogram may predict who can benefit from adaptive radiotherapy in patients with local advanced-NSCLC patients.

BACKGROUND: Although adaptive radiotherapy (ART) has many advantages, ART is not universal in the clinical appliance due to the consumption of a lot of labor, and economic burden. It is necessary to explore a CT stimulation-based radiomics model for screening who can get more benefits from ART in locally advanced non-small cell lung cancer (NSCLC) patients. METHOD: 183 cases of NSCLC patients receiving concurrent chemoradiotherapy with an adaptive approach were enrolled as a primary cohort, while 28 cases from another hospital served as an independent external validation cohort. Tumor regression assessment was conducted based on GTV reduction (Criteria A) or according to RECIST Version 1.1(Criteria B). The radiomics features were extracted by the "PyRadiomics" package and further screened by the LASSO method. Then, logistic regression was used to establish the model. Bootstrap and external validation were applied to verify the stability of the model. The receiver operating characteristic (ROC) curve was delineated to assess the predictive efficacy of the radiomics model. Dose-volume histograms were quantitatively compared between the initial and composite ART plans. Clinical endpoints included overall survival (OS) and progression-free survival (PFS). RESULT: There were no significant differences in clinical features between tumor regression-resistant (RR) and tumor regression-sensitivity (RS) groups. The AUC values of the Criteria A model and Criteria B model were 0.767 and 0.771, respectively. Bootstrapping validation and external validation confirmed the stability of models. In all patients, there was a significant benefit of ART in the lung, heart, cord, and esophagus compared to non-ART, particularly in RS patients. Furthermore, PFS and OS from ART were significantly longer in RS as defined by Criterion B than in RR patients with the same ART application. CONCLUSION: CT-based radiomics can screen out the patients who can gain more benefits from ART, which contribute to guiding and popularizing the application of ART strategy in the clinic within economic benefits and feasibility.

Adaptive intensity-modulated radiotherapy with simultaneous integrated boost for stage III non-small cell lung cancer: Is a routine adaptation beneficial?

PURPOSE: Tumor and anatomical changes during radiotherapy have been observed in stage III non-small cell lung cancer (NSCLC) from many previous studies. We hypothesized that a routinely scheduled adaptive radiotherapy would have clinical important dose benefits to lower the risk of toxicities, without increasing the tumor recurrences. METHODS: We retrospectively reviewed 92 consecutive patients with inoperable stage III NSCLC between November 2017 and March 2019. All eligible patients should received simultaneously integrated boost (SIB) using intensity-modulated radiation therapy (IMRT). A mid-treatment CT simulation and a new adapted plan were routinely given after the first 20 fractions. The organs at risk (OARs) were delineated per RTOG 1106 atlas. Dose-volume histograms were quantitatively compared between the initial and composite adaptive plans. Logistic regression was applied to analyze the dose-response relationship. Clinical endpoints included acute symptomatic radiation pneumonitis (RP2) and esophagitis (RE2), local and regional tumor control, and progression-free survival (PFS). RESULTS: Sixty-four eligible patients received adaptive SIB-IMRT were consecutively included. The GTVs reduced by a median of -38.2% after 42 to 44 Gy in 20 fractions of radiotherapy. By adapting to tumor and anatomical changes, dosimetric parameters of OARs decreased significantly. The mean lung dose decreased by an average of -74.8 cGy, and mean esophagus dose was lower by 183.1 cGy. We found grade 2 or higher acute RP in 11 patients (17.2%), and RE2 in 28 patients (43.8%). Commonly used lung and esophagus dose metrics were significantly associated with RP2 and RE2. The adaptation could reduce RP2 probability by 3%, and RE2 risk by 5%. Subgroups with higher OARs dose or larger tumor shrinkage may get more dose and toxicities benefits. The estimated median PFS was 12.5 months from the start of radiotherapy. CONCLUSIONS: We demonstrated that the routinely adaptive SIB-IMRT strategy could significantly reduce the dose to surrounding normal tissues, potentially lower the associated acute RP and RE, without increasing the risk of tumor recurrences.

Radiation-induced lung damage in patients treated with stereotactic body radiotherapy after EGFR-TKIs: is there any difference from stereotactic body radiotherapy alone?

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 EGFRTKIs 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 (TD50) 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.

Intermediate Dose-Volume Parameters, Not Low-Dose Bath, Is Superior to Predict Radiation Pneumonitis for Lung Cancer Treated With Intensity-Modulated Radiotherapy.

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 (VdoseG) and PTV (VdoseP). 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 V20P, V30P, and MLDP (smaller Ps) than V5G and V5P. The largest bootstrapped AUC was identified for the V30P. We found a trend of better discriminating performance for the V20P and V30P, and MLDP than the V5G and V5P according to the higher values in AUC, IDI, and NRI analysis. To limit RP2 incidence less than 20%, the V30P cutoff was 14.5%. CONCLUSIONS: This study identified the intermediate dose-volume parameters V20P and V30P with better prediction performance for acute RP2 than low-dose metrics V5G and V5P. Among all studied predictors, the V30P had the best discriminating power, and should be considered as a supplement to the traditional dose constraints in lung cancer treated with IMRT.

Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients.

BACKGROUND: Lung dose-volume histogram (DVH) in radiotherapy could be calculated from multiple normal lung definitions. The lung dosimetric parameters generated from various approaches are significantly different. However, limited evidence shows which definition should be used to more accurately predict radiation pneumonitis (RP). We aimed to compare the RP prediction accuracy of dosimetric parameters from three lung volume methods in lung cancer patients treated with Intensity-Modulated Radiation Therapy (IMRT). METHODS: We retrospectively reviewed 183 consecutive lung cancer patients treated with IMRT from January 2014 to October 2017. The normal lungs were defined by total bilateral lung volume (Total Lung), excluding PTV (Lung-PTV) or PGTV (Lung-PGTV). V5, V20, and mean lung dose (MLD) have been extracted from three definitions. The primary endpoint was acute grade 2 or higher RP (RP2). Correlation between RP2 and dose parameters were analyzed by logistic regression. We evaluated prediction performance using area under the receiver operating characteristic curve (AUC) and normal tissue complication probability (NTCP) model. RESULTS: Twenty-six patients (14.2%) developed acute RP2 after IMRT treatment. Significant dosimetric differences were found between any 2-paired lung volumes (Ps < 0.001). To limit RP2 incidence less than 20%, the cutoff MLDs were 12.5 Gy, 14.2 Gy, and 15.0 Gy, respectively, for Lung-PTV, Lung-PGTV, and Total Lung methods. There were 54% (13% vs. 20%) and 45% (20% vs. 29%) RP2 probability variances detected at each MLD cutoff points from Lung-PTV and Lung-PGTV definitions. The best RP prediction performance was found in MLD from Lung-PTV method (AUC = 0.647), which is significantly better (P = 0.006) than the MLD from Lung-PGTV method (AUC = 0.609). CONCLUSION: There are significant differences in acute RP2 rate prediction using dosimetric parameters from various normal lung definitions. Excluding PTV from total lung volume may be more accurate and promising to predict acute symptomatic radiation pneumonitis in IMRT treated lung cancer patients.