Online Adaptive Proton Therapy Facilitated by Artificial Intelligence-Based Autosegmentation in Pencil Beam Scanning Proton Therapy.

PURPOSE: Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy. Artificial intelligence (AI)-based autosegmentation can increase the efficiency and accuracy. Linear energy transfer (LET)-based biological effect evaluation can potentially mitigate possible adverse events caused by high LET. New spot arrangement based on the verification computed tomography (vCT) can further improve the replan quality. We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with patients with prostate cancer. METHODS AND MATERIALS: AI-based autosegmentation tool AccuContour (Manteia) was seamlessly integrated into oAPT. Initial spot arrangement tool on the vCT for reoptimization was implemented using raytracing. An LET-based biological effect evaluation tool was developed to assess the overlap region of high dose and high LET in selected organs at risk. Eleven patients with prostate cancer were retrospectively selected to verify the efficacy and efficiency of the proposed oAPT workflow. The time cost of each component in the workflow was recorded for analysis. RESULTS: The verification plan showed significant degradation of the clinical target volume coverage and rectum and bladder sparing due to the interfractional anatomical changes. Reoptimization on the vCT resulted in great improvement of the plan quality. No overlap regions of high dose and high LET distributions were observed in bladder or rectum in replans. Three-dimensional γ analyses in patient-specific quality assurance confirmed the accuracy of the replan doses before delivery (γ passing rate, 99.57% ± 0.46%) and after delivery (98.59% ± 1.29%). The robustness of the replans passed all clinical requirements. The average time for the complete execution of the workflow was 9.12 ± 0.85 minutes, excluding manual intervention time. CONCLUSIONS: The AI-facilitated oAPT workflow demonstrated to be both efficient and effective by generating a replan that significantly improved the plan quality in prostate cancer treated with pencil beam scanning proton therapy.

Radiotherapy toxicity prediction using knowledge-constrained generalized linear model.

Radiation therapy (RT) is a frontline approach to treating cancer. While the target of radiation dose delivery is the tumor, there is an inevitable spill of dose to nearby normal organs causing complications. This phenomenon is known as radiotherapy toxicity. To predict the outcome of the toxicity, statistical models can be built based on dosimetric variables received by the normal organ at risk (OAR), known as Normal Tissue Complication Probability (NTCP) models. To tackle the challenge of the high dimensionality of dosimetric variables and limited clinical sample sizes, statistical models with variable selection techniques are viable choices. However, existing variable selection techniques are data-driven and do not integrate medical domain knowledge into the model formulation. We propose a knowledge-constrained generalized linear model (KC-GLM). KC-GLM includes a new mathematical formulation to translate three pieces of domain knowledge into non-negativity, monotonicity, and adjacent similarity constraints on the model coefficients. We further propose an equivalent transformation of the KC-GLM formulation, which makes it possible to solve the model coefficients using existing optimization solvers. Furthermore, we compare KC-GLM and several well-known variable selection techniques via a simulation study and on two real datasets of prostate cancer and lung cancer, respectively. These experiments show that KC-GLM selects variables with better interpretability, avoids producing counter-intuitive and misleading results, and has better prediction accuracy.

Long-Term Prospective Outcomes of Intensity Modulated Radiotherapy for Locally Advanced Lung Cancer: A Secondary Analysis of a Randomized Clinical Trial.

Importance: The optimal radiotherapy technique for unresectable locally advanced non-small cell lung cancer (NSCLC) is controversial, so evaluating long-term prospective outcomes of intensity-modulated radiotherapy (IMRT) is important. Objective: To compare long-term prospective outcomes of patients receiving IMRT and 3-dimensional conformal radiotherapy (3D-CRT) with concurrent carboplatin/paclitaxel for locally advanced NSCLC. Design, Setting, and Participants: A secondary analysis of a prospective phase 3 randomized clinical trial NRG Oncology-RTOG 0617 assessed 483 patients receiving chemoradiotherapy (3D-CRT vs IMRT) for locally advanced NSCLC based on stratification. Main Outcomes and Measures: Long-term outcomes were analyzed, including overall survival (OS), progression-free survival (PFS), time to local failure, development of second cancers, and severe grade 3 or higher adverse events (AEs) per Common Terminology Criteria for Adverse Events, version 3. The percentage of an organ volume (V) receiving a specified amount of radiation in units of Gy is reported as V(radiation dose). Results: Of 483 patients (median [IQR] age, 64 [57-70] years; 194 [40.2%] female), 228 (47.2%) received IMRT, and 255 (52.8%) received 3D-CRT (median [IQR] follow-up, 5.2 [4.8-6.0] years). IMRT was associated with a 2-fold reduction in grade 3 or higher pneumonitis AEs compared with 3D-CRT (8 [3.5%] vs 21 [8.2%]; P = .03). On univariate analysis, heart V20, V40, and V60 were associated with worse OS (hazard ratios, 1.06 [95% CI, 1.04-1.09]; 1.09 [95% CI, 1.05-1.13]; 1.16 [95% CI, 1.09-1.24], respectively; all P < .001). IMRT significantly reduced heart V40 compared to 3D-CRT (16.5% vs 20.5%; P < .001). Heart V40 (<20%) had better OS than V40 (≥20%) (median [IQR], 2.5 [2.1-3.1] years vs 1.7 [1.5-2.0] years; P < .001). On multivariable analysis, heart V40 (≥20%), was associated with worse OS (hazard ratio, 1.34 [95% CI, 1.06-1.70]; P = .01), whereas lung V5 and age had no association with OS. Patients receiving IMRT and 3D-CRT had similar rates of developing secondary cancers (15 [6.6%] vs 14 [5.5%]) with long-term follow-up. Conclusions and Relevance: These findings support the standard use of IMRT for locally advanced NSCLC. IMRT should aim to minimize lung V20 and heart V20 to V60, rather than constraining low-dose radiation bath. Lung V5 and age were not associated with survival and should not be considered a contraindication for chemoradiotherapy. Trial Registration: ClinicalTrials.gov Identifier: NCT00533949.

Radiotherapy for Metastatic Epidural Spinal Cord Compression with Increased Doses: Final Results of the RAMSES-01 Trial.

Patients with metastatic epidural spinal cord compression (MESCC) and favorable survival prognoses may benefit from radiation doses exceeding 10 × 3.0 Gy. In a multi-center phase 2 trial, patients receiving 15 × 2.633 Gy (41.6 Gy10) or 18 × 2.333 Gy (43.2 Gy10) were evaluated for local progression-free survival (LPFS), motor/sensory functions, ambulatory status, pain, distress, toxicity, and overall survival (OS). They were compared (propensity score-adjusted Cox regression) to a historical control group (n = 266) receiving 10 × 3.0 Gy (32.5 Gy10). In the phase 2 cohort, 50 (of 62 planned) patients were evaluated for LPFS. Twelve-month rates of LPFS and OS were 96.8% and 69.9%, respectively. Motor and sensory functions improved in 56% and 57.1% of patients, and 94.0% were ambulatory following radiotherapy. Pain and distress decreased in 84.4% and 78.0% of patients. Ten and two patients experienced grade 2 and 3 toxicities, respectively. Phase 2 patients showed significantly better LPFS than the control group (p = 0.039) and a trend for improved motor function (p = 0.057). Ambulatory and OS rates were not significantly different. Radiotherapy with 15 × 2.633 Gy or 18 × 2.333 Gy was well tolerated and appeared superior to 10 × 3.0 Gy.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy.

BACKGROUND: Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities. PURPOSE: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS: PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs-at-risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s. CONCLUSIONS: An accurate and efficient deep learning-augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.

Sublobar Resection, Stereotactic Body Radiation Therapy, and Percutaneous Ablation Provide Comparable Outcomes for Lung Metastasis-Directed Therapy.

BACKGROUND: Prolonged survival of patients with metastatic disease has furthered interest in metastasis-directed therapy (MDT). RESEARCH QUESTION: There is a paucity of data comparing lung MDT modalities. Do outcomes among sublobar resection (SLR), stereotactic body radiation therapy (SBRT), and percutaneous ablation (PA) for lung metastases vary in terms of local control and survival? STUDY DESIGN AND METHODS: Medical records of patients undergoing lung MDT at a single cancer center between January 2015 and December 2020 were reviewed. Overall survival, local progression, and toxicity outcomes were collected. Patient and lesion characteristics were used to generate multivariable models with propensity weighted analysis. RESULTS: Lung MDT courses (644 total: 243 SLR, 274 SBRT, 127 PA) delivered to 511 patients were included with a median follow-up of 22 months. There were 47 local progression events in 45 patients, and 159 patients died. Two-year overall survival and local progression were 80.3% and 63.3%, 83.8% and 9.6%, and 4.1% and 11.7% for SLR, SBRT, and PA, respectively. Lesion size per 1 cm was associated with worse overall survival (hazard ratio, 1.24; P = .003) and LP (hazard ratio, 1.50; P < .001). There was no difference in overall survival by modality. Relative to SLR, there was no difference in risk of local progression with PA; however, SBRT was associated with a decreased risk (hazard ratio, 0.26; P = .023). Rates of severe toxicity were low (2.1%-2.6%) and not different among groups. INTERPRETATION: This study performs a propensity weighted analysis of SLR, SBRT, and PA and shows no impact of lung MDT modality on overall survival. Given excellent local control across MDT options, a multidisciplinary approach is beneficial for patient triage and longitudinal management.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy.

PURPOSE: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s.

Incremental retraining, clinical implementation, and acceptance rate of deep learning auto-segmentation for male pelvis in a multiuser environment.

BACKGROUND: Deep learning auto-segmentation (DLAS) models have been adopted in the clinic; however, they suffer from performance deterioration owing to the clinical practice variability. Some commercial DLAS software provide an incremental retraining function that enables users to train a custom model using their institutional data to account for clinical practice variability. PURPOSE: This study was performed to evaluate and implement the commercial DLAS software with the incremental retraining function for definitive treatment of patients with prostate cancer in a multi-user environment. METHODS: CT-based target organs and organs-at-risk (OAR) delineation of 215 prostate cancer patients were utilized. The performance of three commercial DLAS software built-in models was validated with 20 patients. A retrained custom model was developed using 100 patients and evaluated on the remaining data (n = 115). Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and surface DSC (SDSC) were utilized for quantitative evaluation. A multi-rater qualitative evaluation was blindly performed with a five-level scale. Visual inspection was performed in consensus and non-consensus unacceptable cases to identify the failure modes. RESULTS: Three commercial DLAS vendor built-in models achieved sub-optimal performance in 20 patients. The retrained custom model had a mean DSC of 0.82 for prostate, 0.48 for seminal vesicles (SV), and 0.92 for rectum, respectively. This represents a significant improvement over the built-in model with DSC of 0.73, 0.37, and 0.81 for the corresponding structures. Compared to the acceptance rate of 96.5% and consensus unacceptable rate (i.e., both reviewers rated as unacceptable) of 3.5% achieved by manual contours, the custom model achieved a 91.3% acceptance rate and 8.7% consensus unacceptable rate. The failure modes of retrained custom model were attributed to the following: cystogram (n = 2), hip prosthesis (n = 2), low dose rate brachytherapy seeds (n = 2), air in endorectal balloon(n = 1), non-iodinated spacer (n = 2), and giant bladder(n = 1). CONCLUSION: The commercial DLAS software with the incremental retraining function was validated and clinically adopted for prostate patients in a multi-user environment. AI-based auto-delineation of the prostate and OARs is shown to achieve improved physician acceptance, overall clinical utility, and accuracy.

Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer.

BACKGROUND: Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application. PURPOSE: A deep-learning-based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc. METHODS: A deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre-trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per-voxel absolute CT-number-difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively. RESULTS: The two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per-voxel absolute CT-number-difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5 , within ±0.06% for total lung V5 , ≤1.5cGy[RBE] for heart and esophagus Dmean , and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed. CONCLUSION: A deep neural network-based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer.

PURPOSE: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. METHODS: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). RESULTS: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. CONCLUSION: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.

NCCN Guidelines® Insights: Non-Small Cell Lung Cancer, Version 2.2023.

The NCCN Guidelines for Non-Small Cell Lung Cancer (NSCLC) provide recommendations for management of disease in patients with NSCLC. These NCCN Guidelines Insights focus on neoadjuvant and adjuvant (also known as perioperative) systemic therapy options for eligible patients with resectable NSCLC.

First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients.

PURPOSE: To assess the clinical acceptability of a commercial deep-learning-based auto-segmentation (DLAS) prostate model that was retrained using institutional data for delineation of the clinical target volume (CTV) and organs-at-risk (OARs) for postprostatectomy patients, accounting for clinical and imaging protocol variations. METHODS AND MATERIALS: CTV and OARs of 109 prostate-bed patients were used to evaluate the performance of the vendor-trained model and custom retrained DLAS models using different training quantities. Two new models for OAR structures were retrained (n = 30, 60 data sets), while separate models were trained for a new CTV structure (n = 30, 60, 90 data sets), with the remaining data sets used for testing (n = 49, 19). The dice similarity coefficient (DSC), Hausdorff distance, and mean surface distance were evaluated. Six radiation oncologists performed a qualitative evaluation scoring both preference and clinical utility for blinded structure sets. Physician consensus data sets identified from the qualitative evaluation were used toward a separate CTV model. RESULTS: Both the 30- and 60-case retrained OAR models had median DSC values between 0.91 to 0.97, improving significantly over the vendor-trained model for all OARs except the penile bulb. The brand new 60-case CTV model had a median DSC of 0.70 improving significantly over the 30-case model. DLAS (60-case model) and manual contours were blinded and evaluated by physicians with contours deemed acceptable or precise for 87% and 94% of cases for DLAS and manual delineations, respectively. DLAS-generated CTVs were scored precise or acceptable in 54% of cases, compared with the manual delineation value of 73%. The 30-case physician consensus CTV model did not show a significant difference compared with the randomly selected models. CONCLUSIONS: Custom retraining using institutional data leads to performance improvement in the clinical utility and accuracy of DLAS for postprostatectomy patients. A small number of data sets are sufficient for building an institutional site-specific DLAS OAR model, as well as for training new structures. Data indicates the workload for identifying training data sets could be shared among groups for the male pelvic region, making it accessible to clinics of all sizes.

Clinical Outcomes of Stereotactic Radiosurgery-Related Radiation Necrosis in Patients with Intracranial Metastasis from Melanoma.

Background: Radiation necrosis (RN) is a clinically relevant complication of stereotactic radiosurgery (SRS) for intracranial metastasis (ICM) treatments. Radiation necrosis development is variable following SRS. It remains unclear if risk factors for and clinical outcomes following RN may be different for melanoma patients. We reviewed patients with ICM from metastatic melanoma to understand the potential impact of RN in this patient population. Methods: Patients who received SRS for ICM from melanoma at Mayo Clinic Arizona between 2013 and 2018 were retrospectively reviewed. Data collected included demographics, tumor characteristics, radiation parameters, prior surgical and systemic treatments, and patient outcomes. Radiation necrosis was diagnosed by clinical evaluation including brain magnetic resonance imaging (MRI) and, in some cases, tissue evaluation. Results: Radiation necrosis was diagnosed in 7 (27%) of 26 patients at 1.6 to 38 months following initial SRS. Almost 92% of all patients received systemic therapy and 35% had surgical resection prior to SRS. Patients with RN trended toward having larger ICM and a prior history of surgical resection, although statistical significance was not reached. Among patients with resection, those who developed RN had a longer period between surgery and SRS start (mean 44 vs 33 days). Clinical improvement following treatment for RN was noted in 2 (29%) patients. Conclusions: Radiation necrosis is relatively common following SRS for treatment of ICM from metastatic melanoma and clinical outcomes are poor. Further studies aimed at mitigating RN development and identifying novel approaches for treatment are warranted.

Personalization of Radiation Therapy in the Primary Treatment of Malignant Epidural Spinal Cord Compression (MESCC).

"True" malignant epidural spinal cord compression (MESCC) is used here to describe a lesion compressing of infiltrating the spinal cord associated with neurologic deficits. Radiotherapy alone is the most common treatment, for which several dose-fractionation regimens are available including single-fraction, short-course and longer-course regimens. Since these regimens are similarly effective regarding functional outcomes, patients with poor survival are optimally treated with short-course or even single-fraction radiotherapy. Longer-course radiotherapy results in better local control of malignant epidural spinal cord compression. Since most in-field recurrences occur 6 months or later, local control is particularly important for longer-term survivors who, therefore, should receive longer-course radiotherapy. It is important to estimate survival prior to treatment, which is facilitated by scoring tools. Radiotherapy should be supplemented by corticosteroids, if safely possible. Bisphosphonates and RANK-ligand inhibitors may improve local control. Selected patients can benefit from upfront decompressive surgery. Identification of these patients is facilitated by prognostic instruments considering degree of compression, myelopathy, radio-sensitivity, spinal stability, post-treatment ambulatory status, and patients' performance status and survival prognoses. Many factors including patients' preferences must be considered when designing personalized treatment regimens.

Short Communication: Results of a Consensus Conference on Radiotherapy for Brain and Bone Metastases Within the Interreg-Project TreaT.

BACKGROUND/AIM: Differences between radiotherapy for metastases in Northern Germany and Southern Denmark were previously identified, which led to a consensus conference. PATIENTS AND METHODS: A consensus conference was held between three centers to harmonize radiotherapy regimens for bone and brain metastases. RESULTS: Centers agreed on 1×8 Gy for painful bone metastases in patients with poor or intermediate survival prognoses and 10×3 Gy for favorable-prognosis patients. For complicated bone metastases, 5-6×4 Gy was preferred for poor-prognosis, 10×3 Gy for intermediate-prognosis, and longer-course radiotherapy for favorable-prognosis patients. For ≥5 brain metastases, centers agreed on whole-brain irradiation (WBI) with 5×4 Gy in poor-prognosis and longer-course regimens in other patients. For single brain lesions and patients with 2-4 lesions and intermediate/favorable prognoses, fractionated stereotactic radiotherapy (FSRT) or radiosurgery were recommended. No consensus was reached for 2-4 lesions in poor-prognosis patients; two centers preferred FSRT, one center WBI. Preferred radiotherapy regimens were similar for different age groups including elderly and very elderly patients, but age-specific survival scores were recommended. CONCLUSION: The consensus conference was successful, since harmonization of radiotherapy regimens was achieved for 32 of 33 possible situations.

Prognostic factors in resected pulmonary carcinoid tumors: A retrospective study with 10 years of follow­up.

The objective of the present study was to characterize the difference in 10-year carcinoid-specific survival (CSS) and disease-free survival (DFS) among patients with resected pulmonary typical carcinoid (TC) and atypical carcinoid (AC). Patients diagnosed with pulmonary carcinoid tumors (PCT) between January 1, 1997, and December 31, 2016, were identified. All patients underwent video-assisted thoracoscopic surgery or thoracotomy with thoracic lymphadenectomy. Cumulative CSS was estimated using the Kaplan-Meier model. The analysis of hazard ratios (HRs) and 95% confidence intervals (CIs) was performed using univariate and multivariate Cox proportional hazards models. A total of 404 patients with PCT were included in the present study. The 10-year CSS and DFS rates of patients with AC were significantly worse than those of patients with TC (49.1 vs. 86.8% and 52.2 vs. 92.6%, respectively; P<0.001). In the CSS multivariate analysis, older age and lymph node involvement (HR, 2.45; P=0.022) were associated with worse survival in AC, while age, male sex, M1 stage, cigarette smoking and inadequate N2 lymphadenectomy were associate with worse survival in TC. In the recurrence multivariate analysis, N1-3 stage (HR, 2.62; 95% CI, 1.16-5.95; P=0.018) and inadequate N2 lymphadenectomy (HR, 2.13; 95% CI, 1.04-4.39; P=0.041) were associated with an increase in recurrence in AC, while male sex (HR, 3.72; 95% CI, 1.33-10.42; P=0.010) and M1 stage (HR, 14.93; 95% CI, 4.77-46.77; P<0.001) were associated with an increase in recurrence in TC. In conclusion, patients with AC tumors had significantly worse CSS and DFS rates compared with patients with TC. The degree of nodal involvement in AC was a prognostic marker, in contrast to that in TC. Inadequate lymphadenectomy increased the risk of recurrence in AC and mortality in TC, although surgical approaches did not have a significant impact. The present study therefore emphasizes the importance of mediastinal nodal dissection in patients with PCTs.

Preliminary Analysis of a Phase II Trial of Stereotactic Body Radiation Therapy for Prostate Cancer With High-Risk Features After Radical Prostatectomy.

Purpose: There are limited data regarding using stereotactic body radiation therapy (SBRT) in the postprostatectomy setting. Here, we present a preliminary analysis of a prospective phase II trial that aimed to evaluate the safety and efficacy of postprostatectomy SBRT for adjuvant or early salvage therapy. Materials and Methods: Between May 2018 and May 2020, 41 patients fulfilled inclusion criteria and were stratified into 3 groups: group I (adjuvant), prostate-specific antigen (PSA) < 0.2 ng/mL with high-risk features including positive surgical margins, seminal vesicle invasion, or extracapsular extension; group II (salvage), with PSA ≥ 0.2 ng/mL but < 2 ng/mL; or group III (oligometastatic), with PSA ≥ 0.2 ng/mL but < 2 ng/mL and up to 3 sites of nodal or bone metastases. Androgen deprivation therapy was not offered to group I. Androgen deprivation therapy was offered for 6 months for group II and 18 months for group III patients. SBRT dose to the prostate bed was 30 to 32 Gy in 5 fractions. Baseline-adjusted physician reported toxicities (Common Terminology Criteria for Adverse Events), patient reported quality-of-life (Expanded Prostate Index Composite, Patient-Reported Outcome Measurement Information System), and American Urologic Association scores were evaluated for all patients. Results: The median follow-up was 23 months (range, 10-37). SBRT was adjuvant in 8 (20%) patients, salvage in 28 (68%), and salvage with the presence of oligometastases in 5 (12%) patients. Urinary, bowel, and sexual quality of life domains remained high after SBRT. Patients tolerated SBRT with no grade 3 or higher (3+) gastrointestinal or genitourinary toxicities. The baseline adjusted acute and late toxicity grade 2 genitourinary (urinary incontinence) rate was 2.4% (1/41) and 12.2% (5/41). At 2 years, clinical disease control was 95%, and biochemical control was 73%. Among the 2 clinical failures, 1 was a regional node and the other a bone metastasis. Oligometastatic sites were salvaged successfully with SBRT. There were no in-target failures. Conclusions: Postprostatectomy SBRT was very well tolerated in this prospective cohort, with no significant effect on quality of life metrics postirradiation, while providing excellent clinical disease control.

Early Identification of Pneumonitis in Patients Irradiated for Lung Cancer-Final Results of the PARALUC Trial.

Radiotherapy of lung cancer may cause pneumonitis that generally occurs weeks or months following therapy and can be missed. This prospective trial aimed to pave the way for a mobile application (app) allowing early diagnosis of pneumonitis. The primary goal was the identification of the optimal cut-off of a score to detect pneumonitis of grade ≥2 after radiotherapy for lung cancer. Based on the severity of symptoms (cough, dyspnea, fever), scoring points were 0−9. Receiver operating characteristic (ROC)-curves were used to describe the sensitivity and specificity. The area under the ROC-curve (AUC) was calculated to judge the accuracy of the score, Youden-index was employed to define the optimal cut-off. Until trial termination, 57 of 98 patients were included. Eight of 42 patients evaluable for the primary endpoint (presence or absence of radiation pneumonitis) experienced pneumonitis. AUC was 0.987 (0.961−1.000). The highest sensitivity was achieved with 0−4 points (100%), followed by 5 points (87.5%), highest specificity with 5−6 points (100%). The highest Youden-index was found for 5 points (87.5%). The rate of patient satisfaction with the symptom-based scoring system was 93.5%. A cut-off of 5 points was identified as optimal to differentiate between pneumonitis and no pneumonitis. Moreover, pneumonitis was significantly associated with an increase of ≥3 points from baseline (p < 0.0001). The scoring system provided excellent accuracy and high patient satisfaction. Important foundations for the development of a mobile application were laid.

Comparison of Two Cisplatin Regimens for Chemoradiation in Patients With Squamous-cell Carcinoma of the Head and Neck.

BACKGROUND/AIM: Many patients with squamous-cell carcinoma of the head and neck receive cisplatin-based chemoradiation. This retrospective study compared two chemoradiation programs to help identify the optimal cisplatin-regimen. PATIENTS AND METHODS: Forty-one patients assigned to chemoradiation with two cycles of 20 mg/m2/days(d)1-5 were compared to 78 patients assigned to chemoradiation with two cycles of 25 mg/m2/d1-4. Groups were compared for toxicity, loco-regional control (LRC), and survival. RESULTS: Both treatments were associated with similar rates of oral mucositis, radiation dermatitis, xerostomia, nausea, decreased renal function, and hematotoxicity. The cisplatin-regimen had no significant impact on LRC (p=0.41) or survival (p=0.85). Survival was significantly worse with radiotherapy interruptions (>1 week) or discontinuation (p<0.001) and administration of <80% of the planned cisplatin dose (p<0.001). CONCLUSION: Both cisplatin-regimens did not differ significantly regarding toxicities, LRC, and survival. It is important to avoid interruption or discontinuation of radiotherapy and to administer ≥80% of planned cisplatin.

Evaluation of the Impact of Smoking and Alcohol Consumption on Toxicity and Outcomes of Chemoradiation for Head and Neck Cancer.

BACKGROUND/AIM: Smoking and alcohol abuse may impair outcomes of chemoradiation for squamous cell head and neck cancer (SCCHN). Potential associations with toxicity, loco-regional control (LRC), and overall survival (OS) were investigated. PATIENTS AND METHODS: Ninety-six patients were retrospectively analyzed for impacts of pre-radiotherapy (pre-RT) smoking history, smoking during radiotherapy, and pre-RT alcohol abuse on toxicity, LRC, and OS. RESULTS: A trend was found for associations between pre-RT smoking history and grade ≥2 dermatitis. Smoking during radiotherapy was significantly associated with grade ≥3 mucositis and showed trends regarding grade ≥2 mucositis and dermatitis. On univariate analyses, smoking during radiotherapy was negatively associated with LRC and OS, pre-RT alcohol abuse with OS, and >40 pack years with LRC and OS. In multivariate analyses, smoking during radiotherapy remained significant for decreased OS, and pack years showed a trend. CONCLUSION: Smoking during radiotherapy was an independent predictor of OS and associated with increased toxicity. Thus, it is important to stop smoking prior to the start of radiotherapy.