Induction-concurrent chemoradiotherapy with or without sintilimab in patients with locoregionally advanced nasopharyngeal carcinoma in China (CONTINUUM): a multicentre, open-label, parallel-group, randomised, controlled, phase 3 trial.

BACKGROUND: Anti-PD-1 therapy and chemotherapy is a recommended first-line treatment for recurrent or metastatic nasopharyngeal carcinoma, but the role of PD-1 blockade remains unknown in patients with locoregionally advanced nasopharyngeal carcinoma. We assessed the addition of sintilimab, a PD-1 inhibitor, to standard chemoradiotherapy in this patient population. METHODS: This multicentre, open-label, parallel-group, randomised, controlled, phase 3 trial was conducted at nine hospitals in China. Adults aged 18-65 years with newly diagnosed high-risk non-metastatic stage III-IVa locoregionally advanced nasopharyngeal carcinoma (excluding T3-4N0 and T3N1) were eligible. Patients were randomly assigned (1:1) using blocks of four to receive gemcitabine and cisplatin induction chemotherapy followed by concurrent cisplatin radiotherapy (standard therapy group) or standard therapy with 200 mg sintilimab intravenously once every 3 weeks for 12 cycles (comprising three induction, three concurrent, and six adjuvant cycles to radiotherapy; sintilimab group). The primary endpoint was event-free survival from randomisation to disease recurrence (locoregional or distant) or death from any cause in the intention-to-treat population. Secondary endpoints included adverse events. This trial is registered with ClinicalTrials.gov (NCT03700476) and is now completed; follow-up is ongoing. FINDINGS: Between Dec 21, 2018, and March 31, 2020, 425 patients were enrolled and randomly assigned to the sintilimab (n=210) or standard therapy groups (n=215). At median follow-up of 41·9 months (IQR 38·0-44·8; 389 alive at primary data cutoff [Feb 28, 2023] and 366 [94%] had at least 36 months of follow-up), event-free survival was higher in the sintilimab group compared with the standard therapy group (36-month rates 86% [95% CI 81-90] vs 76% [70-81]; stratified hazard ratio 0·59 [0·38-0·92]; p=0·019). Grade 3-4 adverse events occurred in 155 (74%) in the sintilimab group versus 140 (65%) in the standard therapy group, with the most common being stomatitis (68 [33%] vs 64 [30%]), leukopenia (54 [26%] vs 48 [22%]), and neutropenia (50 [24%] vs 46 [21%]). Two (1%) patients died in the sintilimab group (both considered to be immune-related) and one (<1%) in the standard therapy group. Grade 3-4 immune-related adverse events occurred in 20 (10%) patients in the sintilimab group. INTERPRETATION: Addition of sintilimab to chemoradiotherapy improved event-free survival, albeit with higher but manageable adverse events. Longer follow-up is necessary to determine whether this regimen can be considered as the standard of care for patients with high-risk locoregionally advanced nasopharyngeal carcinoma. FUNDING: National Natural Science Foundation of China, Key-Area Research and Development Program of Guangdong Province, Natural Science Foundation of Guangdong Province, Overseas Expertise Introduction Project for Discipline Innovation, Guangzhou Municipal Health Commission, and Cancer Innovative Research Program of Sun Yat-sen University Cancer Center. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Association Between Lymphopenia and Survival Outcomes in Esophageal Carcinoma Patients Receiving Combined Immunotherapy and Chemoradiotherapy.

BACKGROUND: To investigate the association between absolute lymphocyte count (ALC) nadir and survival outcomes in esophageal squamous cell carcinoma (ESCC) patients who received definitive chemoradiotherapy (CRT) combined with anti-PD-1 immunotherapy, as well as to explore clinical characteristics and dosimetric parameters that affect ALC nadir during CRT. PATIENTS AND METHODS: Patients with ESCC (n = 602) who underwent definitive CRT were analyzed, of whom 166 received combined anti-PD-1 immunotherapy and CRT. Changes in ALC and survival were compared between patients with and without immunotherapy. Propensity score matching (PSM) was performed to minimize the effects of confounding factors. Low ALC was defined as nadir of <0.33 × 103 cells/µL during CRT (top tertile). Univariate and multivariate logistic regression were used to identify predictors of low ALC nadir. RESULTS: Patients with immunotherapy had significantly higher ALC in the first 3 weeks during CRT and higher ALC nadir than those without. Overall survival was more favorable in patients with immunotherapy both before and after PSM. After a median follow-up of 12.1 months, patients with low ALC during CRT had a worse progression-free survival (PFS) (P = .026). In multivariate analysis, low ALC remained a significant prognostic factor for PFS. Planning target volume (PTV) and heart V5 were revealed to be independent predictors of low ALC. CONCLUSIONS: The addition of anti-PD-1 immunotherapy to definitive CRT could mitigate the decline of ALC during radiotherapy and might prolong survival. Low ALC nadir was correlated to worse PFS, larger PTV, and higher heart V5 in patients receiving combined immunotherapy and CRT.

Efficacy and Dose-Response Relationship of Stereotactic Body Radiotherapy for Abdominal Lymph Node Metastases from Hepatocellular Carcinoma.

BACKGROUND: The objective of this study was to investigate the treatment efficacy of stereotactic body radiotherapy (SBRT) and evaluate the influence of radiation dose on local control and survival in patients with abdominal lymph node metastases (LNM) from hepatocellular carcinoma (HCC). PATIENTS AND METHODS: Between 2010 and 2020, data of 148 patients with HCC with abdominal LNM, including 114 who underwent SBRT and 34 who received conventional fractionation radiation therapy (CFRT), were collected. A total radiation dose of 28-60 Gy was delivered in 3-30 fractions, with a median biologic effective dose (BED) of 60 Gy (range, 39-105 Gy). Freedom from local progression (FFLP) and overall survival (OS) rates were analyzed. RESULTS: With a median follow-up of 13.6 months (range, 0.4-96.0 months), the 2-year FFLP and OS rates of the entire cohort were 70.6% and 49.7%, respectively. Median OS of the SBRT group was longer than the CFRT group (29.7 vs. 9.9 months, P = .007). A dose-response relationship was observed between local control and BED in either the entire cohort or the SBRT subgroup. Patients who received SBRT with a BED ≥60 Gy had significantly higher 2-year FFLP and OS rates than those who received a BED <60 Gy (80.1% vs. 63.4%, P = .004; 68.3% vs. 33.0%, P < .001). On multivariate analysis, BED was an independent prognostic factor for both FFLP and OS. CONCLUSIONS: SBRT achieved satisfactory local control and survival with feasible toxicities in patients with HCC with abdominal LNM. Moreover, the findings of this large series suggest a dose-response relationship between local control and BED.

Preliminary study on dosimetry characteristics of a novel cylindrical dose verification system.

OBJECTIVE: To develop a novel ionization chamber array dosimetry system, study its dosimetry characteristics, and perform preliminary tests for plan dose verification. METHODS: The dosimetry characteristics of this new array were tested, including short-term and long-term reproducibility, dose linearity, dose rate dependence, field size dependence, and angular dependence. The open field and MLC field plans were designed for dose testing. Randomly select 30 patient treatment plans (10 intensity-modulated radiation therapy [IMRT] plans and 20 volumetric modulated arc therapy [VMAT] plans) that have undergone dose verification using Portal Dosimetry to perform verification measurement and evaluate dose verification test results. RESULTS: The dosimetry characteristics of the arrays all performed well. The gamma passing rates (3%/2 mm) were more than 96% for the combined open field and MLC field plans. The average gamma pass rates were (99.54 ± 0.58)% and (96.70 ± 3.41)% for the 10 IMRT plans and (99.32 ± 0.89)% and (94.91 ± 6.01)% for the 20 VMAT plans at the 3%/2 mm and 2%/2 mm criteria, respectively, which is similar to the Portal Dosimetry's measurement results. CONCLUSIONS: This novel ionization chamber array demonstrates good dosimetry characteristics and is suitable for clinical IMRT and VMAT plan verifications.

Plan quality and treatment efficiency assurance of two VMAT optimization for cervical cancer radiotherapy.

To investigate the difference of the fluence map optimization (FMO) and Stochastic platform optimization (SPO) algorithm in a newly-introduced treatment planning system (TPS). METHODS: 34 cervical cancer patients with definitive radiation were retrospectively analyzed. Each patient has four plans: FMO with fixed jaw plans (FMO-FJ) and no fixed jaw plans (FMO-NFJ); SPO with fixed jaw plans (SPO-FJ) and no fixed jaw plans (SPO-NFJ). Dosimetric parameters, Modulation Complexity Score (MCS), Gamma Pass Rate (GPR) and delivery time were analyzed among the four plans. RESULTS: For target coverage, SPO-FJ plans are the best ones (P ≤ 0.00). FMO plans are better than SPO-NFJ plans (P ≤ 0.00). For OARs sparing, SPO-FJ plans are better than FMO plans for mostly OARs (P ≤ 0.04). Additionally, SPO-FJ plans are better than SPO-NFJ plans (P ≤ 0.02), except for rectum V45Gy. Compared to SPO-NFJ plans, the FMO plans delivered less dose to bladder, rectum, colon V40Gy and pelvic bone V40Gy (P ≤ 0.04). Meanwhile, the SPO-NFJ plans showed superiority in MU, delivery time, MCS and GPR in all plans. In terms of delivery time and MCS, the SPO-FJ plans are better than FMO plans. FMO-FJ plans are better than FMO-NFJ plans in delivery efficiency. MCSs are strongly correlated with PCTV length, which are negatively with PCTV length (P ≤ 0.03). The delivery time and MUs of the four plans are strongly correlated (P ≤ 0.02). Comparing plans with fixed or no fixed jaw in two algorithms, no difference was found in FMO plans in target coverage and minor difference in Kidney_L Dmean, Mu and delivery time between PCTV width≤15.5 cm group and >15.5 cm group. For SPO plans, SPO-FJ plans showed more superiority in target coverage and OARs sparing than the SPO-NFJ plans in the two groups. CONCLUSIONS: SPO-FJ plans showed superiority in target coverage and OARs sparing, as well as higher delivery efficiency in the four plans.

Lymphopenia During Definitive Chemoradiotherapy in Esophageal Squamous Cell Carcinoma: Association with Dosimetric Parameters and Patient Outcomes.

BACKGROUND: The objective of this study was to investigate the relationship between clinical characteristics, as well as dosimetric parameters, and the risk of treatment-related lymphopenia in esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy (CRT). MATERIALS AND METHODS: Clinical characteristics and dosimetric parameters were collected from 436 patients with ESCC who received definitive CRT from 2010 through 2017. Absolute lymphocyte counts (ALCs) were obtained before, during, and 1 month after CRT. Grade 4 (G4) lymphopenia was defined as ALC <0.2 × 109 /L during CRT. Logistic regression analysis was used to evaluate the effect of each factor on predicting G4 lymphopenia. The relationship between lymphopenia and overall survival (OS) was examined, and a nomogram was developed to predict OS. RESULTS: G4 lymphopenia was observed in 103 patients (23.6%) during CRT. Multivariate analysis indicated that planning target volume (PTV), lung V10 , heart V10 , performance status, and pretreatment lymphopenia were significant risk factors for G4 lymphopenia. Patients with G4 lymphopenia had significantly worse survival than those without. Based on multivariate analysis, clinical TNM stage, radiotherapy modality, pretreatment ALC, and G4 lymphopenia were predictive of OS and were incorporated into the nomogram, yielding a concordance index of 0.71. CONCLUSIONS: G4 lymphopenia during definitive CRT was associated with larger PTVs, higher lung V10 and heart V10 , and worse survival. IMPLICATIONS FOR PRACTICE: The purpose of this study was to investigate the relationship between clinical characteristics, as well as dosimetric parameters, and the risk of treatment-related lymphopenia in 436 patients with esophageal squamous cell carcinoma who received definitive chemoradiotherapy. Grade 4 (G4) lymphopenia was observed in 23.6% of patients during radiotherapy. G4 lymphopenia was associated with larger planning target volumes, higher lung V10 and heart V10 , and worse survival. Then, a nomogram was built based on multivariate analysis, yielding excellent performance to predict overall survival. Prospective studies are needed to investigate potential approaches for mitigating severe lymphopenia, which may ultimately convert into survival benefits.

Modeling the risk of radiation pneumonitis in esophageal squamous cell carcinoma treated with definitive chemoradiotherapy.

BACKGROUND: To develop and validate a nomogram for the prediction of symptomatic radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC) who received definitive concurrent chemoradiotherapy. METHODS: Clinical factors, dose-volume histogram parameters, and pulmonary function parameters were collected from 402 ESCC patients between 2010 and 2017, including 321 patients in the primary cohort and 81 in the validation cohort. The end-point was the occurrence of symptomatic RP (grade ≥ 2) within the first 12 months after radiotherapy. Univariate and multivariate logistic regression analyses were applied to evaluate the predictive value of each factor for RP. A prediction model was generated in the primary cohort, which was internally validated to assess its performance. RESULTS: In the primary cohort, 31 patients (9.7%) experienced symptomatic RP. Based on logistic regression model, patients with larger planning target volumes (PTVs) or higher lung V20 had a higher predictive risk of RP, whereas the overall risk was substantially higher for three-dimensional conformal radiotherapy (3DCRT) than intensity-modulated radiotherapy. On multivariate analysis, independent predictive factors for RP were smoking history (P = 0.035), radiotherapy modality (P < 0.001), PTV (P = 0.039), and lung V20 (P < 0.001), which were incorporated into the nomogram. The areas under the receiver operating characteristic curve of the nomogram in the primary and validation cohorts were 0.772 and 0.900, respectively, which were superior to each predictor alone. CONCLUSIONS: Non-smoking status, 3DCRT, lung V20 (> 27.5%), and PTV (≥ 713.0 cc) were significantly associated with a higher risk of RP. A nomogram was built with satisfactory prediction ability.

Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.

BACKGROUND: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compared with those generated through atlas-based automatic segmentation. MATERIALS AND METHODS: An encoder-decoder U-Net neural network was produced. The trained deep CNN performed the automatic segmentation of CT images for 36 cases of lung cancer. The Dice similarity coefficient (DSC), the mean surface distance (MSD) and the 95% Hausdorff distance (95% HD) were calculated, with manual segmentation results used as the standard, and were compared with the results obtained through atlas-based segmentation. RESULTS: For the heart, lungs and liver, both the deep CNN-based and atlas-based techniques performed satisfactorily (average values: 0.87 < DSC < 0.95, 1.8 mm < MSD < 3.8 mm, 7.9 mm < 95% HD <11 mm). For the spinal cord and the oesophagus, the two methods had statistically significant differences. For the atlas-based technique, the average values were 0.54 < DSC < 0.71, 2.6 mm < MSD < 3.1 mm and 9.4 mm < 95% HD <12 mm. For the deep CNN-based technique, the average values were 0.71 < DSC < 0.79, 1.2 mm < MSD <2.2 mm and 4.0 mm < 95% HD < 7.9 mm. CONCLUSION: Our results showed that automatic segmentation based on a deep convolutional neural network enabled us to complete automatic segmentation tasks rapidly. Deep convolutional neural networks can be satisfactorily adapted to segment OARs during radiation treatment planning for lung cancer patients.

A quantitative method to the analysis of MLC leaf position and speed based on EPID and EBT3 film for dynamic IMRT treatment with different types of MLC.

A quantitative method based on the electronic portal imaging system (EPID) and film was developed for MLC position and speed testing; this method was used for three MLC types (Millennium, MLCi, and Agility MLC). To determine the leaf position, a picket fence designed by the dynamic (DMLC) model was used. The full-width half-maximum (FWHM) values of each gap measured by EPID and EBT3 were converted to the gap width using the FWHM versus nominal gap width relationship. The algorithm developed for the picket fence analysis was able to quantify the gap width, the distance between gaps, and each individual leaf position. To determine the leaf speed, a 0.5 × 20 cm2 MLC-defined sliding gap was applied across a 14 × 20 cm2 symmetry field. The linacs ran at a fixed-dose rate. The use of different monitor units (MUs) for this test led to different leaf speeds. The effect of leaf transmission was considered in a speed accuracy analysis. The difference between the EPID and film results for the MLC position is less than 0.1 mm. For the three MLC types, twice the standard deviation (2 SD) is provided; 0.2, 0.4, and 0.4 mm for gap widths of three MLC types, and 0.1, 0.2, and 0.2 mm for distances between gaps. The individual leaf positions deviate from the preset positions within 0.1 mm. The variations in the speed profiles for the EPID and EBT3 results are consistent, but the EPID results are slightly better than the film results. Different speeds were measured for each MLC type. For all three MLC types, speed errors increase with increasing speed. The analysis speeds deviate from the preset speeds within approximately 0.01 cm s-1 . This quantitative analysis of MLC position and speed provides an intuitive evaluation for MLC quality assurance (QA).

Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry.

Purpose: Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (CNN) using a multichannel input method. Methods: A target conformal plan (TCP) was created based on the maximum planning target volume (PTV). Input data included TCP dose distribution, images, target structures, and organ-at-risk (OAR) information. The role of target conformal plan dose (TCPD) was assessed by comparing the TCPD-CNN (with dose information) and NonTCPD-CNN models (without dose information) using statistical analyses with the ranked Wilcoxon test (P < .05 considered significant). Results: The TCPD-CNN model showed no statistical differences in predicted target indices, except for PTV60, where differences in the D98% indicator were < 0.5%. For OARs, there were no significant differences in predicted results, except for some small-volume or closely located OARs. On comparing TCPD-CNN and NonTCPD-CNN models, TCPD-CNN's dose-volume histograms closely resembled clinical plans with higher similarity index. Mean dose differences for target structures (predicted TCPD-CNN and NonTCPD-CNN results) were within 3% of the maximum prescription dose for both models. TCPD-CNN and NonTCPD-CNN outcomes were 67.9% and 54.2%, respectively. 3D gamma pass rates of the target structures and the entire body were higher in TCPD-CNN than in the NonTCPD-CNN models (P < .05). Additional evaluation on previously unseen volumetric modulated arc therapy plans revealed that average 3D gamma pass rates of the target structures were larger than 90%. Conclusions: This study presents a novel framework for dose distribution prediction using deep learning and multichannel input, specifically incorporating TCPD information, enhancing prediction accuracy for IMRT in NPC treatment.

Generalisation of radiotherapy dose calculation for Monte Carlo algorithm combined with 3D Swin-Unet: a multi-institutional IMRT evaluation.

Objective.Accurate dose calculations are essential prerequisites for precise radiotherapy. The integration of deep learning into dosimetry could consider computational accuracy and efficiency and has potential applicability to clinical dose calculation. The generalisation of a deep learning dose calculation method (hereinafter referred to as TERMA-Monte Carlo network, T-MC net) was evaluated in clinical practice using intensity-modulated radiotherapy (IMRT) plans for various human body regions and multiple institutions, with the Monte Carlo (MC) algorithm serving as a benchmark.Approach. Sixty IMRT plans were selected from four institutions for testing the head and neck, chest and abdomen, and pelvis regions. Using the MC results as the benchmark, the T-MC net calculation results were used to perform three-dimensional dose distribution and dose-volume histogram (DVH) comparisons of the entire body, planning target volume (PTV) and organs at risk (OARs), respectively, and calculate the mean ±95% confidence interval of gamma pass rate (GPR), percentage of agreement (PA) and dose difference ratio (DDR) of dose indices D95, D50, and D5.Main results. For the entire body, the GPRs of 3%/3 mm, 2%/2 mm, 2%/1 mm, and the PA were 99.62 ± 0.32%, 98.50 ± 1.09%, 95.60 ± 2.90% and 97.80 ± 1.12%, respectively. For the PTV, the GPRs of 3%/3 mm, 2%/2 mm, 2%/1 mm and the PA were 98.90 ± 1.00%, 95.78 ± 2.83%, 92.23 ± 4.74% and 98.93 ± 0.62%, respectively. The absolute value of average DDR was less than 1.4%.Significance. We proposed a general dose calculation framework based on deep learning, using the MC algorithm as a benchmark, performing a generalisation test for IMRT treatment plans across multiple institutions. The framework provides high computational speed while maintaining the accuracy of MC and may become an effective dose algorithm engine in treatment planning, adaptive radiotherapy, and dose verification.

Convolution neural network toward Monte Carlo photon dose calculation in radiation therapy.

PURPOSE: The Monte Carlo (MC) algorithm has been widely accepted as the most accurate algorithm for dosimetric calculations under various conditions in radiotherapy. However, the calculation time remains an important obstacle hindering the routine use of MC in clinical settings. In this study, full MC three-dimensional dose distributions were obtained with the inputs of the total energy release per unit mass (TERMA) distributions and the electron density (ED) distributions using a convolutional neural network (CNN). A new Dose-mixup data augmentation routine and training strategy are proposed and applied in the training process. Attempts were made to reduce the calculation time while ensuring that the calculation accuracy is comparable to that of the MC. METHODS: Datasets were generated via the MC with random rectangular field sizes, random iso-centers, and random gantry angles for head and neck computed tomography (CT) images with Mohan 6-MV spectrum photon beams. 1500 samples were generated for the training set, and 150 samples were generated for the validation set. The T-MC Net model was obtained with the Dose-mixup data augmentation routine. The new CTs were used to test the performance of the model in the rectangular fields and the intensity-modulated radiation therapy (IMRT) fields. The mean ± 95% confidence interval of gamma pass rates were calculated. RESULTS: For 150 rectangular field test samples, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 90.11% ± 0.65%, 97.65% ± 0.31%, and 99.16% ± 0.19%, respectively. For the 100 IMRT field test samples, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 96.48% ± 0.28%, 99.14% ± 0.10%, and 99.63% ± 0.06%, respectively. For the 7-fields IMRT plan, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 97.06%, 99.10%, and 99.52%, respectively. For the 9-fields IMRT plan, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 98.16%, 99.61%, and 99.89%, respectively. CONCLUSIONS: The feasibility of calculating dose distribution using a CNN with the TERMA three-dimensional distribution and ED distribution was established. The dosimetric results were comparable to those of the full MC. The accuracy and speed of the proposed approach make it a potential solution for full MC in radiotherapy. This method may be used as an acceleration engine for the dose algorithm and shows great potential for cases where fast dose calculations are needed.

A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer.

Purpose: To investigate the dosimetric impact on target volumes and organs at risk (OARs) when unmodified auto-segmented OAR contours are directly used in the design of treatment plans. Materials and Methods: A total of 127 patients with cervical cancer were collected for retrospective analysis, including 105 patients in the training set and 22 patients in the testing set. The 3D U-net architecture was used for model training and auto-segmentation of nine types of organs at risk. The auto-segmented and manually segmented organ contours were used for treatment plan optimization to obtain the AS-VMAT (automatic segmentations VMAT) plan and the MS-VMAT (manual segmentations VMAT) plan, respectively. Geometric accuracy between the manual and predicted contours were evaluated using the Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), and Hausdorff distance (HD). The dose volume histogram (DVH) and the gamma passing rate were used to identify the dose differences between the AS-VMAT plan and the MS-VMAT plan. Results: Average DSC, MDA and HD95 across all OARs were 0.82-0.96, 0.45-3.21 mm, and 2.30-17.31 mm on the testing set, respectively. The D99% in the rectum and the Dmean in the spinal cord were 6.04 Gy (P = 0.037) and 0.54 Gy (P = 0.026) higher, respectively, in the AS-VMAT plans than in the MS-VMAT plans. The V20, V30, and V40 in the rectum increased by 1.35% (P = 0.027), 1.73% (P = 0.021), and 1.96% (P = 0.008), respectively, whereas the V10 in the spinal cord increased by 1.93% (P = 0.011). The differences in other dosimetry parameters were not statistically significant. The gamma passing rates in the clinical target volume (CTV) were 92.72% and 98.77%, respectively, using the 2%/2 mm and 3%/3 mm criteria, which satisfied the clinical requirements. Conclusions: The dose distributions of target volumes were unaffected when auto-segmented organ contours were used in the design of treatment plans, whereas the impact of automated segmentation on the doses to OARs was complicated. We suggest that the auto-segmented contours of tissues in close proximity to the target volume need to be carefully checked and corrected when necessary.

Comparison of three-dimensional patient-specific dosimetry systems with delivery errors: Toward a new synchronous measurement method.

PURPOSE: This study proposed a synchronous measurement method for patient-specific dosimetry using two three-dimensional dose verification systems with delivery errors. METHODS: Twenty hypofractionated radiotherapy treatment plans for patients with lung cancer were retrospectively reviewed. Monitor unit (MU) changes, leaf in-position errors, and angles of deviation of the collimator were intentionally introduced to investigate the detection sensitivity of the EDose + EPID (EE) and Dolphin + Compass (DC) systems. RESULTS: Both systems accurately detected the MU modifications and had a similar ability to detect leaf in-position errors. The detection of multi-leaf collimator (MLC) errors was difficult for the whole body using different gamma criteria. When the introduced MLC error was 1.0 mm, the numbers of errors detected in the clinical target volume (CTV) by the EE system were 20, 20, and 20 and the numbers of errors detected by the DC system were 18, 19, and 20, at 3%/2 mm, 2%/2 mm, and 1%/1 mm, respectively. The average dose deviation of all DVH parameters exceeded 3%. The gamma and DVH evaluation results remained unchanged for the DC system when different collimator angle errors were introduced. The number of errors detected by the EE system was <11 for each anatomical structure for all gamma criteria. The mean dose deviation of the CTV was not distinguished. CONCLUSIONS: This synchronous measurement approach can effectively eliminate the influence of random errors during treatment. The EE and DC systems reconstruct the three-dimensional dose distribution accurately and are convenient and reliable for dose verification.

Prediction of Pathologic Response to Neoadjuvant Chemoradiotherapy in Patients with Esophageal Squamous Cell Carcinoma Incorporating Hematological Biomarkers.

PURPOSE: This study aimed to develop a nomogram for predicting pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) in patients with esophageal squamous cell carcinoma (ESCC) by integrating hematological biomarkers and clinicopathological characteristics. MATERIALS AND METHODS: Between 2003 and 2017, 306 ESCC patients who underwent neoadjuvant CRT followed by esophagectomy were analyzed. Besides clinicopathological factors, hematological parameters before, during, and after CRT were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR. A nomogram model was built and internally validated. RESULTS: Absolute lymphocyte count (ALC), lymphocyte to monocyte ratio, albumin, hemoglobin, white blood cell, neutrophil, and platelet count generally declined, whereas neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) increased significantly following neoadjuvant CRT. After surgery, 124 patients (40.5%) achieved a pCR. The pCR group demonstrated significantly more favorable survival than the non-pCR group. On multivariate analysis, significant factors associated with pCR included sex, chemotherapy regimen, post-CRT endoscopic finding, pre-CRT NLR, ALC nadir during CRT, and post-CRT PLR, which were incorporated into the prediction model. The nomogram indicated good accuracy in predicting pCR, with a C-index of 0.75 (95% confidence interval, 0.71 to 0.78). CONCLUSION: Female, chemotherapy regimen of cisplatin/vinorelbine, negative post-CRT endoscopic finding, pre-CRT NLR (≤ 2.1), ALC nadir during CRT (> 0.35 ×109/L), and post-CRT PLR (≤ 83.0) were significantly associated with pCR in ESCC patients treated with neoadjuvant CRT. A nomogram incorporating hematological biomarkers to predict pCR was developed and internally validated, showing good predictive performance.

A preliminary study of a photon dose calculation algorithm using a convolutional neural network.

The aim of dose calculation algorithm research is to improve the calculation accuracy while maximizing the calculation efficiency. In this study, the three-dimensional distribution of total energy release per unit mass (TERMA) and the electron density (ED) distribution are considered inputs in a method for calculating the three-dimensional dose distribution based on a convolutional neural network (CNN). Attempts are made to improve the efficiency of the collapsed cone convolution/superposition (CCCS) algorithm while providing an approach to improve the efficiency of other traditional dose calculation algorithms. Twelve sets of computed tomography (CT) images were employed for training. Data sets were generated by the CCCS algorithm with a random beam configuration. For each monoenergetic photon model, 7500 samples were generated for the training set, and 1500 samples were generated for the validation set. Training occurred for 0.5 MeV, 1 MeV, 2 MeV, 3 MeV, 4 MeV, 5 MeV, and 6 MeV monoenergetic photon models. To evaluate the usability under linac conditions, a comparison between CCCS and CNN-Dose was performed for the Mohan 6-MV spectrum for 12 additional new sets of CT images with different anatomies. A total of 1512 test samples were generated. For all anatomies, the mean value, 95% lower confidence limit (LCL) and 95% upper confidence limit (UCL) were 99.56%, 99.51% and 99.61%, respectively, at the 3%/2 mm criteria. The mean value, 95% LCL and 95% UCL were 98.57%, 98.46% and 98.67%, respectively, at the 2%/2 mm criteria. The results meet the relevant clinical requirements. In the proposed methods, the dose distribution of clinical energy can be obtained by TERMA, and the electronic density can be obtained with a CNN. This method can also be used for other traditional dose algorithms and displays potential in treatment planning, adaptive radiation therapy, and in vivo verification.

Propensity score-based comparison of survival and radiation pneumonitis after definitive chemoradiation for esophageal cancer: Intensity-modulated radiotherapy versus three-dimensional conformal radiotherapy.

PURPOSE: To compare survival outcomes and radiation pneumonitis (RP) between intensity-modulated radiotherapy (IMRT) and three-dimensional conformal radiotherapy (3DCRT) in patients with esophageal cancer (EC) who underwent definitive chemoradiation therapy (CRT). METHODS: Clinical characteristics and dose-volume histogram parameters were collected for 388 EC patients who received definitive CRT with either IMRT (n = 297) or 3DCRT (n = 91) from 2010 through 2017. Dosimetric parameters, survival end-points, and symptomatic RP (grade ≥2) were compared between groups. Propensity score matching (PSM) was performed to balance potential confounding factors. Univariate and multivariate logistic regression analyses were applied to identify predictors of RP. RESULTS: Compared with 3DCRT, IMRT was significantly associated with better overall survival (OS; P = 0.001), progression-free survival (PFS; P = 0.008), and distant metastasis-free survival (P = 0.011), but not with locoregional failure-free survival (P = 0.721). Moreover, IMRT demonstrated a remarkably lower risk of RP than 3DCRT (5.4% vs 23.1%, P < 0.001). PSM analysis further confirmed the clinical benefit of IMRT. In the matched cohort, radiation modality was independently correlated with OS and PFS. On multivariate analysis, smoking history (odds ratio [OR]: 4.225, P = 0.002), primary tumor length (OR: 2.764, P = 0.049), radiation modality (OR: 10.760, P < 0.001), planning target volume (OR: 1.004, P < 0.001), and lung V20 (OR: 1.286, P = 0.002) were found to be significant predictors of RP. CONCLUSIONS: Compared with 3DCRT, IMRT was associated with more favorable survival and a reduced risk of RP after definitive CRT, supporting the routine use of IMRT for EC.

Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma.

BACKGROUND AND PURPOSE: To validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved. METHODS AND MATERIALS: A knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated. RESULTS: With comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable. CONCLUSION: The automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.

Comparison of Different Combinations of Irradiation Mode and Jaw Width in Helical Tomotherapy for Nasopharyngeal Carcinoma.

Purpose: To aid in the selection of a suitable combination of irradiation mode and jaw width in helical tomotherapy (HT) for the treatment of nasopharyngeal carcinoma (NPC). Materials and Methods: Twenty patients with NPC who underwent radiotherapy were retrospectively selected. Four plans using a jaw width of 2.5 or 5-cm in dynamic jaw (DJ) or fix jaw (FJ) modes for irradiation were designed (2.5DJ, 2.5FJ, 5.0DJ, and 5.0FJ). The dose parameters of planning target volume (PTV) and organs at risk (OARs) of the plans were compared and analyzed, as well as the beam on time (BOT) and monitor unit (MU). The plans in each group were ranked by scoring the doses received by the OARs and the superity was assessed in combination with the planned BOT and MU. Results: The prescribed dose coverage of PTV met the clinical requirements for all plans in the four groups. The groups using a 2.5-cm jaw width or a DJ mode provided better protection to most OARs, particularly for those at the longitudinal edges of the PTV (P < 0.05). The 2.5DJ group had the best ranking for OAR-dose, followed by the 2.5FJ and 5.0DJ groups with a same score. The BOT and MU of the groups using a 5.0-cm jaw width reduced nearly 45% comparing to those of the 2.5-cm jaw groups. Conclusion: 2.5DJ has the best dose distribution, while 5.0DJ has satisfactory dose distribution and less BOT and MU that related to the leakage dose. Both 2.5DJ or 5DJ were recommended for HT treatment plan for NPC based on the center workload.

A trial for EBT3 film without batch-specific calibration using a neural network.

This note reports a trial to establish an ANN (artificial neural network) method applying to EBT3 films of different batches without batch-specific calibration. Based on Pytorch (Facebook, https://pytorch.org/), a feed-forward ANN model was built to convert the pixel values of scanned images from different batches into absorbed dose. Films from different batches exposed to x-ray doses were digitized in transmission mode on an Epson 11000XL scanner for training and testing. The calculated dose map of TPS (radiation therapy planning system) was used as a label (the desired output) for the ANN model. To verify the performance and generalization of the ANN method, a cross-validation experiment was performed. Using the trained ANN method, the scanned images were converted into absorbed dose maps, and the converted dose maps have good agreement with the calculated dose maps from TPS. For films irradiated via the sliding window mode, the MSEs (mean square errors) of the trained batches were less than [Formula: see text] and the MSEs of the tested batches were less than [Formula: see text]. For patient intensity-modulated radiotherapy (IMRT) films, the γ(3%, 3 mm) between the dose maps obtained from the trained films and TPS exceeded 97.5%. The γ(3%, 3 mm) between most of the dose maps obtained from the tested films and TPS exceeded 97.0%. This shows that it is feasible to establish a method for EBT3 films from certain batches to convert pixel values into an absorbed dose without batch-specific calibration, and the method can be applied to other cases.