BACKGROUND:Body composition is emerging as a prognostic biomarker in cancer and may be associated with treatment tolerance, side-effects, and health-related quality of life (HRQoL). It can be measured from imaging routinely acquired during patient care. We evaluated whether body composition metrics were associated with radiotherapy-related side-effects and HRQoL in patients with prostate or lung cancer using a prospective multicentre dataset. METHODS:Radiotherapy planning computed tomography (CT) scans, patient and disease characteristics, and clinician- and patient-reported side-effects up to 24 months post-treatment were obtained from the REQUITE study. Skeletal muscle and intramuscular adipose tissue were segmented at the L3 and T12 vertebrae for prostate and lung patients respectively using in-house software. Standardised total average toxicity scores captured composite acute and late clinician- and patient-reported side-effects and HRQoL. Gradient boosted machine models were developed for all endpoints with and without body composition variables. Predictor importance rankings and model performance (root mean squared error (RMSE)) were assessed. RESULTS:279 lung and 848 prostate patients were available for analysis. Body composition variables were ranked in the top five most important variables for 9 of 12 endpoints. Body composition variables were ranked higher than body mass index for 9 of 12 endpoints. Adding body composition variables was associated with statistically significant (p < 0.01) but small reductions in apparent/in-sample RMSE across endpoints. CONCLUSIONS:Body composition variables were frequently ranked among important predictors of radiotherapy-related side-effects and HRQoL, but their incremental improvement in apparent model fit was small. These findings suggest that CT-derived body composition may warrant further investigation as an exploratory imaging biomarker, but external validation and demonstration of clinically meaningful incremental value are required before clinical implementation.

PURPOSE:We showed previously that breast cancer patients with certain circadian rhythm genotypes have higher incidences of toxicity when treated with radiotherapy at different times of day. This opens the way to genetically-guided chronomodulation as a cost-effective way to reduce side-effects while preserving treatment efficacy. We aimed to test whether similar time-of-day effects are observed in prostate cancer. EXPERIMENTAL DESIGN:The analysis used the multinational, prospective observational REQUITE prostate cancer cohort (n=1760). All patients received external beam radiotherapy and were followed for two years. Regression analyses were performed in patients with complete data including genotypes and radiotherapy fraction times (n=877). LASSO and logistic regression models incorporated genotypes of SNPs in circadian rhythm genes, time of radiotherapy and their interaction. Primary endpoints were late rectal bleeding and urinary incontinence. RESULTS:Multivariable models show a significant effect of genotype, time of treatment and an interaction between PER3 genotypes and time for both primary endpoints. The 44% of patients with rs696305 C/C genotype are predicted to reduce their risk of rectal bleeding from 11% when treated at 09:00 to 5% at 17:00. Similarly, the analysis predicts a significant reduction in risk of urinary incontinence from 15% to 5% by avoiding treatment in the middle of the day for patients heterozygous for rs172933 (36% of patients). CONCLUSIONS:These results agree with the earlier findings in breast cancer radiotherapy patients. Interaction between genotype and time of treatment suggests groups of patients could reduce side-effects and improve quality-of-life through chronomodulation.

While considerable effort has been devoted to examining how variations in study protocols, acquisition settings, and annotations influence radiomics features, the role of feature selection (FS) methods largely remains unexplored. This study investigates a self-supervised deep sparse autoencoder ensemble (ensembleAE) and a novel Bayesian variant (bayesianAE) for radiomics FS in a small, class-imbalanced framework. Using a cohort of 100 prostate cancer patients under active surveillance, these models were benchmarked against eight classical FS methods. FS stability was assessed both globally and locally in a soft data-perturbation setting. While global stability measured consistency in overall feature ranking, local stability quantified the agreement in selecting top-ranked feature subsets. Among classical methods, wrappers were the least stable, whereas the filter-based Wilcoxon test (WLCX) exhibited high local stability and performance with moderate global stability. Conversely, bayesianAE demonstrated the highest global stability while matching WLCX in local stability and performance. Furthermore, although bayesianAE yielded local stability comparable to that of ensembleAE, it outperformed the latter in terms of global stability with a significantly lower computational burden (95 × faster and consumes 84% less memory). These findings position bayesianAE as a robust alternative to classical FS methods that could support the development of reproducible radiomics signatures.

BACKGROUND:Active surveillance (AS) is standard for early-stage prostate cancer, though intensive monitoring continues due to concerns about reclassification to Grade Group (GG) ⩾ 2. We hypothesized that simple and inexpensive clinical parameters could identify patients with indolent disease for whom less intensive monitoring is safe. METHODS:We analyzed upgrading to ISUP Grade Group (GG) ⩾ 2 in a large cohort of low-risk prostate cancer (PCa) patients on AS. We used mixed-effects logistic regression to develop a model predicting non-upgrading at the next follow-up biopsy, based on routine time-updated clinical-pathological variables. RESULTS:While annual reclassification persisted at a rate between 10.4% and 17.5% up to the 7th year, upgrades were predominantly GG2. Routine parameters powerfully stratified risk. Patients with a very unfavorable Prostate Specific Antigen (PSA) doubling time had a 40.5% upgrading rate versus 9.5% for those with a favorable value. The final model is based on baseline PSA density and number of positive cores, time-updated age and PSA doubling time category, detection of cancer in the last surveillance biopsy. The model showed moderate discrimination (0.73) in predicting non-upgrading. CONCLUSIONS:Many patients on AS have indolent disease but steady upgrading justifies monitoring. A model using simple, routine parameters and PSA doubling time can identify those at minimal risk of reclassification, enabling a safe reduction in biopsy intensity. This advocates for a risk-adaptive AS protocol, reserving advanced tools like MRI or biomarkers for the few higher-risk cases.

BACKGROUND:The role of surgical exploration and frozen sections (FSs) in monorchid patients with testicular nodules is still not well defined. We tested the role of surgical exploration and FSs in monorchid patients and the impact on the chance of testis sparing surgery (TSS). METHODS:We identified 81 consecutive monorchid patients with testicular nodules between 2008 and 2024 candidates to surgical exploration and FSs. The statistical significance of differences in medians and proportions was tested with the Wilcoxon rank sum and Chi-square tests. Multivariable logistic regression models (MLRMs) were used. RESULTS:Testicular lesions number was available in 61 patients and was one in 35 (57.4%) of those, two in 15 (24.6%), three in 7 (11.5%) and more than three in 4 (6.5%). Median larger lesion size was 12 mm (IQR 9-20 mm). FSs were performed in 59 (73%) patients and showed germ-cell tumor (GCT) in 53 (65.4%). Orchidectomy was performed in 68 patients (84%). In 55 of 56 patients (98.3%) definitive histology confirmed FSs. Thirteen (16%) had TSS including 7 patients with seminomatous GCT, of those none had disease relapse at follow-up. At MLRMs older age was associated with lower probability of GCT (Odds Ratio 0.91, Confidence Interval 0.84-0.99, P value 0.03). CONCLUSIONS:FSs are feasible and reliable in monorchid patients following a history of GCT. Nonetheless, TSS is rarely performed, as most of these patients actually have GCT. The few ones who had TSS had excellent oncological results.

PURPOSE:To quantitatively assess how microvascular conditions estimated via a non-invasive method can predict acute side effects after radiotherapy in patients with breast, prostate, and head and neck cancers. MATERIALS AND METHODS:We studied 314 patients, assessing their MicroVascular Health Score (MVHS) before radiotherapy using a sublingual microscope that records videos of red blood cells in microvessels, analyses the microvasculature's functional and geometric properties, and calculates the MVHS. The MVHS was incorporated into a logistic model to predict the risk of developing acute radio-induced both alone and in combination with dose. RESULTS:A clear quantitative association between MVHS and side effects was observed in the overall population (OR = 0.68, p = 0.001). A 1-unit decrease in MVHS was associated with a 31.9% increased risk of radiation-induced symptoms across cancer sites. In the breast subgroup, dose factors significantly affected side effect risk (OR for D200cc to the skin: 1.05 per 1 Gy; OR for D20cc: 1.14 per 1 Gy; OR for V20Gy: 1.01 per 1 cc; all p < 0.05). The D20cc model had the highest AUC (0.69), and adding MVHS further improved it to 0.76. CONCLUSION:The study strongly linked microvascular health to the risk of acute radio-induced symptoms, suggesting that this could inform predictive models and customise treatments based on patient vascular status.

Background:Prospectively collected long-term adverse event data with pre-radiotherapy assessment and regular follow-up reflecting 'real world settings' are still limited. Here, we present the available data on early and long-term outcomes up to eight years after radiotherapy in men with prostate cancer who participated in the international REQUITE cohort study, stratified by treatment modalities and patient characteristics. Methods:Longitudinal data on pre-radiotherapy, acute and late symptoms up to eight years after radiotherapy were available from 1760 non-metastatic prostate cancer patients recruited in seven European countries and the USA. External beam radiotherapy (EBRT) and/or brachytherapy were given with curative intent between 2014 and 2016 according to local regimens. Gastrointestinal (GI) and genitourinary (GU) symptoms including sexual dysfunction were prospectively assessed by healthcare professionals according to CTCAE v4.0. Patient-reported outcomes were collected. Results:The highest incidence of clinician-reported grade ≥ 2 long-term GI and GU symptoms were for proctitis (12.1%) and haematuria (12.9%) among the EBRT-only treated patients, and proctitis (6.4%) and urinary incontinence (32.4%) among the post-prostatectomy/EBRT patients. Incidence of grade 3 adverse events was below 1.5%, with the only exception of late urinary incontinence in post-prostatectomy patients (10.2%). Conclusion:The frequency and distribution of GI and GU symptoms in a real-world multicentre cohort of patients following prostate radiotherapy were comparable to those reported in large interventional trials. Our finding highlights the value of standardised data farming approaches like the REQUITE project. Most patients consented to provide their data for future cancer research which is available on request.

PURPOSE:Genome-wide association studies are the gold standard for identifying SNP associated with rectal toxicity after prostate cancer radiotherapy. However, they often neglect the radiotherapy dose distribution, which is a key contributor to toxicity risk. Here, we combined rectal dose surface maps with genetic data to identify rectal regions in which variants influence dose-toxicity relationships. EXPERIMENTAL DESIGN:Data were analyzed from 1,293 patients with prostate cancer from the REQUITE study. Deep learning rectum contouring ensured consistent segmentation, and rectum lengths were standardized to generate two-dimensional dose surface maps. Patients were categorized based on the presence of risk alleles for three candidate SNP (rs1801516, rs17055178, and rs17630638). Propensity score matching accounted for age, rectal volume, prostate volume, and hormone therapy. Voxel-wise Cox proportional hazards models with permutation testing assessed dose-toxicity associations. RESULTS:Voxel-wise Cox proportional hazards models revealed significant (P < 0.05) dose-toxicity associations in risk allele carriers for all SNP for bowel urgency. Risk regions were consistently in the lower posterior rectum. Higher risk of acute bowel control was identified among carriers of the risk allele for rs17630638. For this SNP, carriers of the risk allele showed a higher risk for late rectal bleeding but a reduced risk for acute rectal bleeding. CONCLUSIONS:This study identified genotype-driven toxicity patterns using spatial dose mapping. By revealing consistent high-risk rectal regions, this approach strengthens the link between genomics and radiotherapy planning. Importantly, modern radiotherapy planning makes it feasible to reduce dose in genetically sensitive patients and move toward more personalized treatment.

Background Overlapping genes are involved with rheumatoid arthritis (RA) and DNA repair pathways. Therefore, we hypothesized that patients with a high polygenic risk score for RA will have an increased risk of radiotherapy toxicity given the involvement of DNA repair.Methods Primary analysis was performed on 1494 prostate cancer, 483 lung cancer, and 1820 breast cancer patients assessed for development of radiotherapy toxicity in the REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) study. Validation cohorts were available from the Radiogenomics Consortium. All patients had undergone curative-intent radiotherapy and were assessed prospectively for toxicity. Germline genomic data was available for all patients, allowing a polygenic risk score to be calculated using 101 RA risk variants. Polygenic risk score was analyzed as a continuous variable and with a more than 90th percentile cutoff. Associations with acute and late standardized total average toxicity (STAT) scores and individual toxicity endpoints were analyzed in multivariable models with preselected adjustment variables.Results Increasing polygenic risk score for RA did not increase the risk of STAT-acute or STAT-late in any cohort. There was an increased risk of late esophagitis in the lung cancer cohort (coefficient = 0.018, P = .01), however this was not validated (P = .79). No individual acute or late toxicity endpoints were statistically significantly associated with polygenic risk score for the prostate or breast cohorts. No statistically significant results were found in the validation cohorts in multivariable models.Conclusions Patients with a high genetic risk for RA do not show increased levels of toxicity after radiotherapy suggesting treatment planning does not need to be modified for such patients.

INTRODUCTION:This study evaluates how model parameter values affect dose-response maps (DRMs) in identifying high-risk bladder subregions associated with late urinary toxicities in prostate cancer patients post-radiotherapy. METHODS:Data from 1808 patients were analyzed for five late bladder toxicities. Baseline scores were subtracted from maximum toxicity at 12 and 24 months and dichotomized into grades ≥ 1 and ≥ 2. Bladders were segmented on computed tomography scans, and dose-surface maps (DSMs) were created on 91 × 90 voxel grids using spherical and cylindrical coordinates. Voxel doses were converted to equivalent dose in 2 Gy fractions (EQD2, α/β 1-3 Gy). Welch's t and Mann-Whitney U equations were applied at each voxel location. Multiple comparisons were corrected via permutation testing (10-10000 iterations), and statistically significant voxels were identified using the 90th and 95th percentiles of Tmax/Umax. Sensitivity of parameters was assessed by varying one parameter at a time, with changes > 400 voxels (∼5% of 8190) classified as large and ≤ 400 as small. RESULTS:Urinary tract obstruction was the only toxicity significantly associated with bladder DSMs, focusing results on this outcome. After baseline adjustment and dichotomization, event/nonevent counts were 62/701 (grade≥1) and 21/742 (grade≥2; N = 763). DRM results showed large effects of toxicity grade threshold, coordinate system, statistical test equation, and Tmax/Umax thresholding. EQD2 α/β showed variable effects, large for cylindrical and small for spherical coordinates, while the number of permutations had only a small effect. CONCLUSIONS:Parameter selection significantly influences high-risk subregion identification in DRMs, emphasizing the need for standardized parameter reporting for meaningful external comparisons.

Background and purpose: Growing evidence suggests that spatial dose variations across the rectal surface influence toxicity risk after radiotherapy. Existing methodologies employ a fixed, arbitrary physical extent for rectal dose mapping, limiting their analysis. We developed a method to standardise rectum contours, unfold them into 2D cylindrical surface maps, and identify subregions where higher doses increase rectal toxicities. Materials and methods: Data of 1,048 patients with prostate cancer from the REQUITE study were used. Deep learning based automatic segmentations were generated to ensure consistency. Rectum length was standardised using linear transformations superior and inferior to the prostate. The automatic contours were validated against the manual contours through contour variation assessment with cylindrical mapping. Voxel-based analysis of the dose surface maps for the manual and automatic contours against individual rectal toxicities was performed using Student's t permutation test and Cox Proportional Hazards Model (CPHM). Significance was defined by permutation testing. Results: Our method enabled the analysis of 1,048 patients using automatic segmentation. Student's t-test showed significance (p < 0.05) in the lower posterior for clinical-reported proctitis and patient-reported bowel urgency. Univariable CPHM identified a 3 % increased risk per Gy for clinician-reported proctitis and a 2 % increased risk per Gy for patient-reported bowel urgency in the lower posterior. No other endpoints were significant. Conclusion: We developed a methodology that unfolds the rectum to a 2D surface map. The lower posterior was significant for clinician-reported proctitis and patient-reported bowel urgency, suggesting that reducing the dose in the region could decrease toxicity risk.

Prostate cancer (PC) survivors frequently experience multiple co-occurring symptoms that adversely affect health-related quality of life (HRQoL). Identifying symptom clusters (SCs) may help to improve symptom management and patient care. The aim of this study is to investigate (1) SCs in PC patients, (2) associations of SCs with HRQoL, and (3) predictors of SCs. We used data from an international, multi-centre, prospective cohort study (REQUITE). SCs were identified from patient-reported outcomes collected with the EORTC Core Quality of Life questionnaire (EORTC QLQ-C30) and pelvic symptom questionnaires. Machine learning techniques identified SCs, associations with HRQoL and SCs predictors. The dataset was divided into training (80

Background and purpose:Radiotherapy dose-response maps (DRM) combine dose-surface maps (DSM) and toxicity outcomes to identify high-risk subregions in organ-at-risk. This study assesses the impact of baseline toxicity correction on the identification of high-risk subregions in dose-response modeling for prostate cancer patients undergoing radiotherapy. Materials and methods:The analysis included 1808 datasets, with 589 exclusions before toxicity-specific data removal. Bladder/rectum were automatically segmented on planning computed tomography scans, DSMs unwrapped into 91x90 voxel grids, and converted to equivalent doses in 2 Gy fractions (EQD2; α/β = 1 Gy). Seventeen late toxicities were assessed with two methods: (i) baseline toxicity subtracted from the maximum of 12- and 24-months toxicity scores, dichotomized at grade 1, and (ii) maximum of 12- and 24-months toxicity scores dichotomized at grade 1. DSMs were split accordingly, and voxel-wise t-values computed using Welch's t-equation. Statistically significant voxels were identified via the 95th percentile of maximum of t-value (Tmax) distribution. Results:Event counts with baseline correction were 82/82/286/226 for urinary tract obstruction/retention/urgency/incontinence, respectively; without baseline correction, they were 93/104/465/361. For bladder DSMs, urinary incontinence, obstruction, retention, and urgency had 1143/186, 1768/1848, 516/0, and 33/0 significant voxels without/with baseline correction. For rectum DSMs, urinary incontinence and tract obstruction had 604/0 and 1980/889 significant voxels without/with baseline correction. However, no significant associations between rectal DSMs and rectum-related toxicities were found. Conclusions:DRM without baseline correction appears more sensitive to high-risk subregions due to higher event counts. Non-linear toxicity grading and multivariable analysis may enhance DRM reliability.