Combination and Optimal Sequencing of Systemic and Locoregional Therapies in Hepatocellular Carcinoma: Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel.

Hepatocellular carcinoma, historically, has had a poor prognosis with very few systemic options. Furthermore, most patients at diagnosis are not surgical candidates. Therefore, locoregional therapy (LRT) has been widely used, with strong data supporting its use. Over the last 15 years, there has been progress in the available systemic agents. This has led to the updated Barcelona Clinic Liver Cancer (BCLC) algorithm's inclusion of these new systemic agents, with advocacy of earlier usage in those who progress on LRT or have tumor characteristics that make them less likely to benefit from LRT. However, neither the adjunct of LRT nor the specific sequencing of combination therapies is addressed directly. This Research Consensus Panel sought to highlight research priorities pertaining to the combination and optimal sequencing of LRT and systemic therapy, assessing the greatest needs across BCLC stages.

Increasing the efficiency of cone-beam CT based delta-radiomics using automated contours to predict radiotherapy-related toxicities in prostate cancer.

Extracting longitudinal image quantitative data, known as delta-radiomics, has the potential to capture changes in a patient's anatomy throughout the course of radiation treatment for prostate cancer. Some of the major challenges of delta-radiomics studies are contouring the structures for individual fractions and accruing patients' data in an efficient manner. The manual contouring process is often time consuming and would limit the efficiency of accruing larger sample sizes for future studies. The problem is amplified because the contours are often made by highly trained radiation oncologists with limited time to dedicate to research studies of this nature. This work compares the use of automated prostate contours generated using a deformable image-based algorithm to make predictive models of genitourinary and changes in total international prostate symptom score in comparison to manually contours for a cohort of fifty patients. Area under the curve of manual and automated models were compared using the Delong test. This study demonstrated that the delta-radiomics models were similar for both automated and manual delta-radiomics models.

Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique.

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS = -0.21, p = 0.001), Decipher (rS = -0.193, p = 0.003), and three-tier risk (rS = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI.

The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients' risk using combined clinical-genomic classification.

Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside.

Background: Immune checkpoint inhibitor (ICI) therapy is first-line treatment for many advanced non-small cell lung cancer (aNSCLC) patients. Predicting response could help guide selection of intensified or alternative anti-cancer regimens. We hypothesized that radiomics and laboratory variables predictive of ICI response in a murine model would also predict response in aNSCLC patients. Methods: Fifteen mice with lung carcinoma tumors implanted in bilateral flanks received ICI. Pre-ICI laboratory and computed tomography (CT) data were evaluated for association with systemic ICI response. Baseline clinical and CT data for 117 aNSCLC patients treated with nivolumab were correlated with overall survival (OS). Models for predicting treatment response were created and subjected to internal cross-validation, with the human model further tested on 42 aNSCLC patients who received pembrolizumab. Results: Models incorporating baseline NLR and identical radiomics (surface-to-mass ratio, average Gray, and 2D kurtosis) predicted ICI response in mice and OS in humans with AUCs of 0.91 and 0.75, respectively. The human model successfully sorted pembrolizumab patients by longer vs. shorter predicted OS (median 35 months vs. 6 months, p=0.026 by log-rank). Discussion: This study advances precision oncology by non-invasively classifying aNSCLC patients according to ICI response using pre-treatment data only. Interestingly, identical radiomics features and NLR correlated with outcomes in the preclinical study and with ICI response in 2 independent patient cohorts, suggesting translatability of the findings. Future directions include using a radiogenomic approach to optimize modeling of ICI response.

Abscopal effect observed in visceral and osseous metastases after liver SBRT in combination with nivolumab and relatlimab for sinonasal mucosal melanoma-a case report.

Background: Primary sinonasal mucosal melanoma (SNMM) is a rare, aggressive histology usually diagnosed at advanced stages and associated with poor prognosis. Evidence regarding etiology, diagnosis, and treatment mainly derives from case reports, retrospective series, and national databases. In the treatment of metastatic melanoma, anti-CTLA-4 and anti-PD-1 checkpoint blockade increased 5-year overall survival from ~10% (prior to 2011) to ~50% (between 2011 and 2016). In March of 2022, the FDA approved the use of relatlimab, a novel anti-LAG3 immune checkpoint inhibitor, for the treatment of melanoma. Case presentation: A 67-year-old woman with locally advanced SNMM underwent debulking surgery, adjuvant RT, and first-line immunotherapy (ImT) with nivolumab but developed local progression. The patient started a second course of ImT with nivolumab and ipilimumab, but this was discontinued after two cycles due to an immune-related adverse event (irAE, hepatitis with elevated liver enzymes). Interval imaging identified visceral and osseous metastases including multiple lesions in the liver and in the lumbar spine. She went on to receive a third course of ImT with nivolumab and the novel agent relatlimab with concurrent stereotactic body radiation therapy (SBRT) to the largest liver tumor only, delivered in five 10-Gy fractions using MRI guidance. A PET/CT performed 3 months after SBRT demonstrated complete metabolic response (CMR) of all disease sites including non-irradiated liver lesions and spinal metastatic sites. After two cycles of the third course of ImT, the patient developed severe immune-related keratoconjunctivitis and ImT was discontinued. Conclusion: This case report describes the first complete abscopal response (AR) in an SNMM histology and the first report of AR following liver SBRT with the use of relatlimab/nivolumab combination ImT for metastatic melanoma in the setting of both visceral and osseous lesions. This report suggests that the combination of SBRT with ImT potentiates the adaptive immune response and is a viable path for immune-mediated tumor rejection. The mechanisms behind this response are hypothesis-generating and remain an area of active research with exceedingly promising potential.

For Hepatocellular Carcinoma Treated with Yttrium-90 Microspheres, Dose Volumetrics on Post-Treatment Bremsstrahlung SPECT/CT Predict Clinical Outcomes.

In transarterial radioembolization (TARE) of hepatocellular carcinoma (HCC) with Yttrium-90 (Y-90) microspheres, recent studies correlate dosimetry from bremsstrahlung single photon emission tomography (SPECT/CT) with treatment outcomes; however, these studies focus on measures of central tendency rather than volumetric coverage metrics commonly used in radiation oncology. We hypothesized that three-dimensional (3D) isodose coverage of gross tumor volume (GTV) is the driving factor in HCC treatment response to TARE and is best assessed using advanced dosimetry techniques applied to nuclear imaging of actual Y-90 biodistribution. We reviewed 51 lobar TARE Y-90 treatments of 43 HCC patients. Dose prescriptions were 120 Gy for TheraSpheres and 85 Gy for SIR-Spheres. All patients underwent post-TARE Y-90 bremsstrahlung SPECT/CT imaging. Commercial software was used to contour gross tumor volume (GTV) and liver on post-TARE SPECT/CT. Y-90 dose distributions were calculated using the Local Deposition Model based on post-TARE SPECT/CT activity maps. Median gross tumor volume (GTV) dose; GTV receiving less than 100 Gy, 70 Gy and 50 Gy; minimum dose covering the hottest 70%, 95%, and 98% of the GTV (D70, D95, D98); mean dose to nontumorous liver, and disease burden (GTV/liver volume) were obtained. Clinical outcomes were collected for all patients by chart and imaging review. HCC treatment response was assessed according to the modified response criteria in solid tumors (mRECIST) guidelines. Kaplan-Meier (KM) survival estimates and multivariate regression analyses (MVA) were performed using STATA. Median survival was 22.5 months for patients achieving objective response (OR) in targeted lesions (complete response (CR) or partial response (PR) per mRECIST) vs. 7.6 months for non-responders (NR, stable disease or disease progression per mRECIST). On MVA, the volume of underdosed tumor (GTV receiving less than 100 Gy) was the only significant dosimetric predictor for CR (p = 0.0004) and overall survival (OS, p = 0.003). All targets with less than CR (n = 39) had more than 20 cc of underdosed tumor. D70 (p = 0.038) correlated with OR, with mean D70 of 95 Gy for responders and 60 Gy for non-responders (p = 0.042). On MVA, mean dose to nontumorous liver trended toward significant association with grade 3+ toxicity (p = 0.09) and correlated with delivered activity (p < 0.001) and burden of disease (p = 0.05). Dosimetric models supplied area under the curve estimates of > 0.80 predicting CR, OR, and ≥grade 3 acute toxicity. Dosimetric parameters derived from the retrospective analysis of post-TARE Y-90 bremsstrahlung SPECT/CT after lobar treatment of HCC suggest that volumetric coverage of GTV, not a high mean or median dose, is the driving factor in treatment response and that this is best assessed through the analysis of actual Y-90 biodistribution.

MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy.

Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/ß = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.

Cone-beam CT delta-radiomics to predict genitourinary toxicities and international prostate symptom of prostate cancer patients: a pilot study.

For prostate cancer (PCa) patients treated with definitive radiotherapy (RT), acute and late RT-related genitourinary (GU) toxicities adversely impact disease-specific quality of life. Early warning of potential RT toxicities can prompt interventions that may prevent or mitigate future adverse events. During intensity modulated RT (IMRT) of PCa, daily cone-beam computed tomography (CBCT) images are used to improve treatment accuracy through image guidance. This work investigated the performance of CBCT-based delta-radiomic features (DRF) models to predict acute and sub-acute International Prostate Symptom Scores (IPSS) and Common Terminology Criteria for Adverse Events (CTCAE) version 5 GU toxicity grades for 50 PCa patients treated with definitive RT. Delta-radiomics models were built using logistic regression, random forest for feature selection, and a 1000 iteration bootstrapping leave one analysis for cross validation. To our knowledge, no prior studies of PCa have used DRF models based on daily CBCT images. AUC of 0.83 for IPSS and greater than 0.7 for CTCAE grades were achieved as early as week 1 of treatment. DRF extracted from CBCT images showed promise for the development of models predictive of RT outcomes. Future studies will include using artificial intelligence and machine learning to expand CBCT sample sizes available for radiomics analysis.

Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy.

We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.

Erring Characteristics of Deformable Image Registration-Based Auto-Propagation for Internal Target Volume in Radiotherapy of Locally Advanced Non-Small Cell Lung Cancer.

Purpose: Respiratory motion of locally advanced non-small cell lung cancer (LA-NSCLC) adds to the challenge of targeting the disease with radiotherapy (RT). One technique used frequently to alleviate this challenge is an internal gross tumor volume (IGTV) generated from manual contours on a single respiratory phase of the 4DCT via the aid of deformable image registration (DIR)-based auto-propagation. Through assessing the accuracy of DIR-based auto-propagation for generating IGTVs, this study aimed to identify erring characteristics associated with the process to enhance RT targeting in LA-NSCLC. Methods: 4DCTs of 19 patients with LA-NSCLC were acquired using retrospective gating with 10 respiratory phases (RPs). Ground-truth IGTVs (GT-IGTVs) were obtained through manual segmentation and union of gross tumor volumes (GTVs) in all 10 phases. IGTV auto-propagation was carried out using two distinct DIR algorithms for the manually contoured GTV from each of the 10 phases, resulting in 10 separate IGTVs for each patient per each algorithm. Differences between the auto-propagated IGTVs (AP-IGTVs) and their corresponding GT-IGTVs were assessed using Dice coefficient (DICE), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), and percent volume difference (PVD) and further examined in relation to anatomical tumor location, RP, and deformation index (DI) that measures the degree of deformation during auto-propagation. Furthermore, dosimetric implications due to the analyzed differences between the AP-IGTVs and GT-IGTVs were assessed. Results: Findings were largely consistent between the two algorithms: DICE, MSSD, ASSD, and PVD showed no significant differences between the 10 RPs used for propagation (Kruskal-Wallis test, ps > 0.90); MSSD and ASSD differed significantly by tumor location in the central-peripheral and superior-inferior dimensions (ps < 0.0001) while only in the central-peripheral dimension for PVD (p < 0.001); DICE, MSSD, and ASSD significantly correlated with the DI (Spearman's rank correlation test, ps < 0.0001). Dosimetric assessment demonstrated that 79% of the radiotherapy plans created by targeting planning target volumes (PTVs) derived from the AP-IGTVs failed prescription constraints for their corresponding ground-truth PTVs. Conclusion: In LA-NSCLC, errors in DIR-based IGTV propagation present to varying degrees and manifest dependences on DI and anatomical tumor location, indicating the need for personalized consideration in designing RT internal target volume.

Prophylactic Radiotherapy for Prevention of Heterotopic Ossification After Periacetabular Fractures: A Review of Efficacy and Associated Conditions.

Prophylactic radiotherapy (XRT) is a commonly used treatment to decrease heterotopic ossification (HO) in patients with traumatic hip injuries. We conducted a retrospective review of patients at risk for HO who underwent XRT. Of the patients reviewed, 27.3% developed radiographic HO, 11.2% developed symptoms, and 2.0% required resection surgery. Patients were divided into primary (n = 71) and secondary prophylaxis (n = 27) cohorts. In the primary group, 25.0% developed radiographic HO, 5.6% developed symptoms, and 0 required surgery. In the secondary cohort, 33.3% of patients developed new radiographic HO, and 25.9% were symptomatic: four had a Brooker score of 3, and three had a score of 4 (p = 0.03), and 7.4% required surgical resection. (Journal of Surgical Orthopaedic Advances 31(2):113-118, 2022).

Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer.

Background/Hypothesis: MRI-guided online adaptive radiotherapy (MRI-g-OART) improves target coverage and organs-at-risk (OARs) sparing in radiation therapy (RT). For patients with locally advanced cervical cancer (LACC) undergoing RT, changes in bladder and rectal filling contribute to large inter-fraction target volume motion. We hypothesized that deep learning (DL) convolutional neural networks (CNN) can be trained to accurately segment gross tumor volume (GTV) and OARs both in planning and daily fractions' MRI scans. Materials/Methods: We utilized planning and daily treatment fraction setup (RT-Fr) MRIs from LACC patients, treated with stereotactic body RT to a dose of 45-54 Gy in 25 fractions. Nine structures were manually contoured. MASK R-CNN network was trained and tested under three scenarios: (i) Leave-one-out (LOO), using the planning images of N- 1 patients for training; (ii) the same network, tested on the RT-Fr MRIs of the "left-out" patient, (iii) including the planning MRI of the "left-out" patient as an additional training sample, and tested on RT-Fr MRIs. The network performance was evaluated using the Dice Similarity Coefficient (DSC) and Hausdorff distances. The association between the structures' volume and corresponding DSCs was investigated using Pearson's Correlation Coefficient, r. Results: MRIs from fifteen LACC patients were analyzed. In the LOO scenario the DSC for Rectum, Femur, and Bladder was >0.8, followed by the GTV, Uterus, Mesorectum and Parametrium (0.6-0.7). The results for Vagina and Sigmoid were suboptimal. The performance of the network was similar for most organs when tested on RT-Fr MRI. Including the planning MRI in the training did not improve the segmentation of the RT-Fr MRI. There was a significant correlation between the average organ volume and the corresponding DSC (r = 0.759, p = 0.018). Conclusion: We have established a robust workflow for training MASK R-CNN to automatically segment GTV and OARs in MRI-g-OART of LACC. Albeit the small number of patients in this pilot project, the network was trained to successfully identify several structures while challenges remain, especially in relatively small organs. With the increase of the LACC cases, the performance of the network will improve. A robust auto-contouring tool would improve workflow efficiency and patient tolerance of the OART process.

Lung Inflammation Predictors in Combined Immune Checkpoint-Inhibitor and Radiation Therapy-Proof-of-Concept Animal Study.

PURPOSE: Combined radiotherapy (RT) and immune checkpoint-inhibitor (ICI) therapy can act synergistically to enhance tumor response beyond what either treatment can achieve alone. Alongside the revolutionary impact of ICIs on cancer therapy, life-threatening potential side effects, such as checkpoint-inhibitor-induced (CIP) pneumonitis, remain underreported and unpredictable. In this preclinical study, we hypothesized that routinely collected data such as imaging, blood counts, and blood cytokine levels can be utilized to build a model that predicts lung inflammation associated with combined RT/ICI therapy. MATERIALS AND METHODS: This proof-of-concept investigational work was performed on Lewis lung carcinoma in a syngeneic murine model. Nineteen mice were used, four as untreated controls and the rest subjected to RT/ICI therapy. Tumors were implanted subcutaneously in both flanks and upon reaching volumes of ~200 mm3 the animals were imaged with both CT and MRI and blood was collected. Quantitative radiomics features were extracted from imaging of both lungs. The animals then received RT to the right flank tumor only with a regimen of three 8 Gy fractions (one fraction per day over 3 days) with PD-1 inhibitor administration delivered intraperitoneally after each daily RT fraction. Tumor volume evolution was followed until tumors reached the maximum size allowed by the Institutional Animal Care and Use Committee (IACUC). The animals were sacrificed, and lung tissues harvested for immunohistochemistry evaluation. Tissue biomarkers of lung inflammation (CD45) were tallied, and binary logistic regression analyses were performed to create models predictive of lung inflammation, incorporating pretreatment CT/MRI radiomics, blood counts, and blood cytokines. RESULTS: The treated animal cohort was dichotomized by the median value of CD45 infiltration in the lungs. Four pretreatment radiomics features (3 CT features and 1 MRI feature) together with pre-treatment neutrophil-to-lymphocyte (NLR) ratio and pre-treatment granulocyte-macrophage colony-stimulating factor (GM-CSF) level correlated with dichotomized CD45 infiltration. Predictive models were created by combining radiomics with NLR and GM-CSF. Receiver operating characteristic (ROC) analyses of two-fold internal cross-validation indicated that the predictive model incorporating MR radiomics had an average area under the curve (AUC) of 0.834, while the model incorporating CT radiomics had an AUC of 0.787. CONCLUSIONS: Model building using quantitative imaging data, blood counts, and blood cytokines resulted in lung inflammation prediction models justifying the study hypothesis. The models yielded very-good-to-excellent AUCs of more than 0.78 on internal cross-validation analyses.

Predictive Value of Delta-Radiomics Texture Features in 0.35 Tesla Magnetic Resonance Setup Images Acquired During Stereotactic Ablative Radiotherapy of Pancreatic Cancer.

Purpose: The purpose of this work is to explore delta-radiomics texture features for predicting response using setup images of pancreatic cancer patients treated with magnetic resonance image guided (MRI-guided) stereotactic ablative radiotherapy (SBRT). Methods: The total biological effective dose (BED) was calculated for 30 patients treated with MRI-guided SBRT that delivered physical doses of 30-60 Gy in three to five fractions. Texture features were then binned into groups based upon BED per fraction by dividing BED by the number of fractions. Delta-radiomics texture features were calculated after delivery of 20 Gy BED (BED20 features) and 40 Gy BED (BED40 features). A random forest (RF) model was constructed using BED20 and then BED40 features to predict binary outcome. During model training, the Gini Index, a measure of a variable's importance for accurate prediction, was calculated for all features, and the two features that ranked the highest were selected for internal validation. The two features selected from each bin were used in a bootstrapped logistic regression model to predict response and performance quantified using the area under the receiver operating characteristic curve (AUC). This process was an internal validation analysis. Results: After RF model training, the Gini Index was highest for gray-level co-occurrence matrix-based (GLCM) sum average, and neighborhood gray tone difference matrix-based (NGTDM) busyness for BED20 features and gray-level size zone matrix-based (GLSZM) large zones low gray-level emphasis and gray-level run length matrix-based (GLRLM) run percentage was selected from the BED40-based features. The mean AUC obtained using the two BED20 features was AUC = 0.845 with the 2.5 percentile and 97.5 percentile values ranging from 0.794 to 0.856. Internal validation of the BED40 delta-radiomics features resulted in a mean AUC = 0.567 with a 2.5 and 97.5 percentile range of 0.502-0.675. Conclusion: Early changes in treatment quantified with the BED20 delta-radiomics texture features in low field images acquired during MRI-guided SBRT demonstrated better performance in internal validation than features calculated later in treatment. Further analysis of delta-radiomics texture analysis in low field MRI is warranted.

Chemoradiation followed by adjuvant durvalumab in stage III non-small cell lung cancer: Real-world comparison of treatment outcomes to historical controls treated with chemoradiation alone.

OBJECTIVE: Compare outcomes in patients with stage III non-small cell lung cancer (NSCLC) treated with chemoradiation and adjuvant durvalumab to historical controls treated with chemoradiation alone. METHODS: The records of patients with stage III NSCLC treated with definitive chemoradiation ± adjuvant durvalumab were reviewed retrospectively. Primary endpoints were progression free survival (PFS), overall survival (OS), and adverse events (AE). RESULTS: Between September 2009 and September 2020, 215 patients were treated with concurrent chemoradiation (n = 144) or concurrent chemoradiation followed by adjuvant durvalumab (n = 71). Compared to historical controls, durvalumab use was associated with improved PFS: median (27 months vs. 10 months, p < 0.0001), 1-year (83.1% vs. 43.8, p < 0.0001); and improved OS; median (not reached vs. 24 months, p < 0.0001), 1-year (85.9% vs. 81.9%, p < 0.0001). Multivariate analysis showed adjuvant durvalumab was associated with increased OS (p = 0.005) and PFS (p = 0.001). Within the durvalumab group, only clinical stage IIIA versus IIIB/C was associated with improved OS (p = 0.049), but not PFS. There was no association between PFS or OS and Eastern Cooperative Oncology Group (ECOG) score, prior history of immune disease, programmed death-ligand 1 (PD-L1) receptor status, delay in starting durvalumab beyond 42 days, or development of an AE. During durvalumab treatment, 63 AE were reported in 52 patients with treatment discontinuation in 11. Pneumonitis was the most common AE reported (n = 35, 49%). Most AE were grade 1-2 (n = 57). Grade 3-4 AE were uncommon (n = 6) and none were grade 5. CONCLUSION: Treatment with adjuvant durvalumab following chemoradiation was associated with improved PFS and OS compared to chemoradiation alone.

Primary Versus Secondary Radiotherapy for Heterotopic Ossification Prevention About the Elbow.

OBJECTIVES: To examine the efficacy and safety of radiotherapy for the prevention of heterotopic ossification (HO) about the elbow. DESIGN: Retrospective chart review. SETTING: Level 1 trauma center. PATIENTS/PARTICIPANTS: Two hundred and twenty-nine patients who received prophylactic radiotherapy (XRT) over a 15-year period were identified. Patients were included if they received XRT to the elbow joint and had at least 12 weeks of follow-up after XRT. Fifty-four patients were ultimately included. INTERVENTION: All patients were treated with a single dose of 7 Gy. Ninety-eight percentage of patients received XRT within 24 hours after surgery, and all patients received XRT within 72 hours after surgery. MAIN OUTCOMES MEASUREMENTS: The primary study measures evaluated were the presence or absence of clinically symptomatic HO and the presence of radiographic HO after XRT to the elbow joint. RESULTS: Eighteen patients were treated with XRT after a traumatic injury requiring surgery (primary prophylaxis), and 36 were treated with XRT after excision surgery to remove HO which had already formed (secondary prophylaxis). In the primary cohort, 16.7% developed symptomatic HO after XRT and 11.1% required surgery to resect the heterotopic bone. In the secondary cohort, 11.1% developed symptomatic HO after surgery and XRT and 5.5% required resection surgery. No secondary malignancies were identified. CONCLUSIONS: Our findings suggest that XRT for elbow HO may be safe and effective for both primary and secondary HO. XRT for HO was not shown to be associated with radiation-induced sarcoma in this series, at least in the short term. Further study in a large patient population with extended follow-up is required to better characterize populations at high risk for development of HO and secondary malignancy. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer.

This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours. Twenty-eight prostate cancer patients enrolled on an institutional review board (IRB) approved protocol were selected. Planning CTs (pCTs) were deformably registered to daily cone-beam CTs (CBCTs) to generate prostate contours (auto contours). The prostate contours were also manually drawn by a physician. Quantitative assessment of deformed versus manually drawn prostate contours on daily CBCT images was performed using Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), difference in center-of-mass position (ΔCM) and difference in volume (ΔVol). Radiomic features from 6 classes were extracted from each contour. Lin's concordance correlation coefficient (CCC) and mean absolute percent difference in radiomic feature-derived data (mean |%Δ|RF) between auto and manual contours were calculated. The mean (± SD) DSC, MDA, ΔCM and ΔVol between the auto and manual prostate contours were 0.90 ± 0.04, 1.81 ± 0.47 mm, 2.17 ± 1.26 mm and 5.1 ± 4.1% respectively. Of the 1,010 fractions under consideration, 94.8% of DIRs were within TG-132 recommended tolerance. 30 radiomic features had a CCC > 0.90 and 21 had a mean |%∆|RF < 5%. Auto-propagation of prostate contours resulted in nearly 95% of DIRs within tolerance recommendations of TG-132, leading to the majority of features being regarded as acceptably robust. The use of auto contours for radiomic feature analysis is promising but must be done with caution.

The Impact of Pelvic Nodal Radiotherapy on Hematologic Toxicity: A Systematic Review with Focus on Leukopenia, Lymphopenia and Future Perspectives in Prostate Cancer Treatment.

INTRODUCTION: Hematologic toxicity (HT), particularly leukopenia, is a common side-effect of oncologic treatments for pelvic malignancies. Pelvic nodal radiotherapy (PNRT) has been associated with HT development mainly through incidental bone marrow (BM) irradiation; however, several questions remain about the clinical impact of radiotherapy-related HT. Herein, we perform a systematic review of the available evidence on PNRT and HT. MATERIALS AND METHODS: A comprehensive systematic literature search was performed through EMBASE. Hand searching and clinicaltrials.gov were also used. RESULTS: While BM-related dose-volume parameters and BM-sparing techniques have been more thoroughly investigated in pelvic malignancies such as cervical, anal, and rectal cancers, the importance of BM as an organ-at-risk has received less attention in prostate cancer treatment. CONCLUSIONS: We examined the available evidence regarding the impact of PNRT on HT, with a focus on prostate cancer treatment. We suggest that BM should be regarded as an organ-at-risk for patients undergoing PNRT.