Inter-institutional variability of knowledge-based plan prediction of left whole breast irradiation.

PURPOSE: Within a multi-institutional project, we aimed to assess the transferability of knowledge-based (KB) plan prediction models in the case of whole breast irradiation (WBI) for left-side breast irradiation with tangential fields (TF). METHODS: Eight institutions set KB models, following previously shared common criteria. Plan prediction performance was tested on 16 new patients (2 pts per centre) extracting dose-volume-histogram (DVH) prediction bands of heart, ipsilateral lung, contralateral lung and breast. The inter-institutional variability was quantified by the standard deviations (SDint) of predicted DVHs and mean-dose (Dmean). The transferability of models, for the heart and the ipsilateral lung, was evaluated by the range of geometric Principal Component (PC1) applicability of a model to test patients of the other 7 institutions. RESULTS: SDint of the DVH was 1.8 % and 1.6 % for the ipsilateral lung and the heart, respectively (20 %-80 % dose range); concerning Dmean, SDint was 0.9 Gy and 0.6 Gy for the ipsilateral lung and the heart, respectively (<0.2 Gy for contralateral organs). Mean predicted doses ranged between 4.3 and 5.9 Gy for the ipsilateral lung and 1.1-2.3 Gy for the heart. PC1 analysis suggested no relevant differences among models, except for one centre showing a systematic larger sparing of the heart, concomitant to a worse PTV coverage, due to high priority in sparing the left anterior descending coronary artery. CONCLUSIONS: Results showed high transferability among models and low inter-institutional variability of 2% for plan prediction. These findings encourage the building of benchmark models in the case of TF-WBI.

Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches.

PURPOSE: Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314). METHODS: The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high p-value at univariate LR and with Spearman ρ>0.8 were excluded; synthesized data of the minority were generated to compensate for class imbalance. Twelve ML methods were considered. Model optimization and sequential backward selection were run to choose the best models with a parsimonious feature number. Finally, feature importance was derived for every model. RESULTS: The model's performance was compared on a training-test dataset over different metrics: the best performance model was LightGBM. Logistic regression with three variables (LR3) selected via bootstrapping showed performances similar to the best-performing models. The AUC of test data is slightly above 0.65 for the best models (highest value: 0.662 with LightGBM). CONCLUSIONS: No model performed the best for all metrics: more complex ML models had better performances; however, models with just three features showed performances comparable to the best models using many (n = 13-19) features.

Skin dose-volume predictors of moderate-severe late side effects after whole breast radiotherapy.

BACKGROUND: Toxicity after whole breast Radiotherapy is a relevant issue, impacting the quality-of-life of a not negligible number of patients. We aimed to develop a Normal Tissue Complication Probability (NTCP) model predicting late toxicities by combining dosimetric parameters of the breast dermis and clinical factors. METHODS: The skin structure was defined as the outer CT body contour's 5 mm inner isotropic expansion. It was retrospectively segmented on a large mono-institutional cohort of early-stage breast cancer patients enrolled between 2009 and 2017 (n = 1066). Patients were treated with tangential-field RT, delivering 40 Gy in 15 fractions to the whole breast. Toxicity was reported during Follow-Up (FU) using SOMA/LENT scoring. The study endpoint was moderate-severe late side effects consisting of Fibrosis-Atrophy-Telangiectasia-Pain (FATP G ≥ 2) developed within 42 months after RT completion. A machine learning pipeline was designed with a logistic model combining clinical factors and absolute skin DVH (cc) parameters as output. RESULTS: The FATP G2 + rate was 3.8 %, with 40/1066 patients experiencing side effects. After the preprocessing of variables, a cross-validation was applied to define the best-performing model. We selected a 4-variable model with Post-Surgery Cosmetic alterations (Odds Ratio, OR = 7.3), Aromatase Inhibitors (as a protective factor with OR = 0.45), V20 Gy (50 % of the prescribed dose, OR = 1.02), and V42 Gy (105 %, OR = 1.09). Factors were also converted into an adjusted V20Gy. CONCLUSIONS: The association between late reactions and skin DVH when delivering 40 Gy/15 fr was quantified, suggesting an independent role of V20 and V42. Few clinical factors heavily modulate the risk.

Single Versus Fractionated Gamma Knife Radiosurgery for Nonfunctioning Pituitary Adenomas Close to the Optic Pathway: A Multicenter Propensity Score Matched Study.

BACKGROUND AND OBJECTIVES: Gamma Knife radiosurgery (GKRS), typically administered in a single session (S-GKRS), is an effective treatment for nonfunctioning pituitary adenoma (NFPA). For lesions close to the optic pathway, the use of hypofractionated radiosurgery is growing. This study seeks to compare the results of S-GKRS vs fractionated-GKRS (F-GKRS) for NFPAs adjacent to the optic pathway. METHODS: Two cohorts of patients with residual or recurrent NFPAs in contact to the optic pathway were retrospectively included in this study: (1) a group of patients who underwent a 3-day course of F-GKRS in Europe and (2) a group of patients treated with S-GKRS in the United States. A propensity score matching (ratio 1:1) was carried out to obtain and compare 2 homogeneous groups of patients with NFPA. RESULTS: A total of 84 patients were included for analysis (42 in the S-GKRS cohort and 42 in the F-GKRS group). The 2 cohorts did not differ for age, sex, number of previous surgical procedure, tumor volume, and follow-up. The mean follow-up was 60.2 ± 37.0 months and 62.4 ± 37.4 months for F-GKRS and S-GKRS cohort, respectively (P = .38). The overall tumor control at last follow-up was achieved in 95.2% and 92.9% of patients in F-GKRS and S-GKRS, respectively (P = .64). The 1-year, 3-year, 5-year, and 7-year progression-free survival rate after F-GKRS was 100%, 97.1%, 97.1%, and 91%, respectively. In the S-GKRS sample, progression-free survival rates were 100%, 100%, 92.5%, and 92.5% at 1, 3, 5, and 7 years after treatment, respectively. Two patients (4.7%) from the F-GKRS cohort and 2 (4.7%) from the S-GKRS cohort sustained visual worsening after radiosurgery (P = 1.0). CONCLUSION: In the management of NFPAs adjacent to the optic pathway both F-GKRS and S-GKRS had comparable outcomes and risks at 7 years. Future prospective studies including larger cohorts with longer follow-up are needed to confirm our results.

Impact of low skeletal muscle mass in oropharyngeal cancer patients treated with radical chemo-radiotherapy: A mono-institutional experience.

AIMS: Low skeletal muscle mass index (SMI) has recently emerged as an independent prognostic factor in oncological patients and it is linked with poor survival and higher treatment toxicity. The present study aims to determine the possible impact of low SMI on survival and acute toxicity in oropharyngeal patients. METHODS: Seventy-six patients with locally advanced oropharyngeal squamous cell carcinoma (stage III-IVC) were treated in our institution with Helical TomoTherapy® (HT - Accuray, Maddison, WI, USA) between 2005 and 2021. All patients received concomitant platinum-based chemotherapy (CT) (at least 200 mg/m2). The SMI was determined using the calculation of cross-sectional area at C3. Twenty patients (26%) presented pre-treatment low SMI, according to Chargi definitions. RESULTS: All patients concluded the treatment. Thirteen patients with low SMI (65%) and 22 patients with normal SMI (39%) presented acute toxicity greater than or equal to grade 3, but this difference was not statistically significant (p-value = 0.25). Overall survival was analyzed in 65 patients, excluding those who finished CT-RT less than six months before the analysis. Overall survival was significantly lower in low SMI versus normal SMI patients (p-value = 0.035). Same difference was observed in N0-N2a patients, suggesting an important role of SMI also in lower nodal burden and putatively better prognosis. CONCLUSIONS: Although the results are limited to a small population, our case series has the advantage to be very homogeneous in patients and treatment characteristics. In our setting, SMI demonstrated a crucial impact on overall survival. Further investigation with larger samples is necessary to confirm our results to improve patient outcomes.

Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning.

Background and purpose: Artificial Intelligence (AI)-based auto-contouring for treatment planning in radiotherapy needs extensive clinical validation, including the impact of editing after automatic segmentation. The aims of this study were to assess the performance of a commercial system for Clinical Target Volumes (CTVs) (prostate/seminal vesicles) and selected Organs at Risk (OARs) (rectum/bladder/femoral heads + femurs), evaluating also inter-observer variability (manual vs automatic + editing) and the reduction of contouring time. Materials and methods: Two expert observers contoured CTVs/OARs of 20 patients in our Treatment Planning System (TPS). Computed Tomography (CT) images were sent to the automatic contouring workstation: automatic contours were generated and sent back to TPS, where observers could edit them if necessary. Inter- and intra-observer consistency was estimated using Dice Similarity Coefficients (DSC). Radiation oncologists were also asked to score the quality of automatic contours, ranging from 1 (complete re-contouring) to 5 (no editing). Contouring times (manual vs automatic + edit) were compared. Results: DSCs (manual vs automatic only) were consistent with inter-observer variability (between 0.65 for seminal vesicles and 0.94 for bladder); editing further improved performances (range: 0.76-0.94). The median clinical score was 4 (little editing) and it was <4 in 3/2 patients for the two observers respectively. Inter-observer variability of automatic + editing contours improved significantly, being lower than manual contouring (e.g.: seminal vesicles: 0.83vs0.73; prostate: 0.86vs0.83; rectum: 0.96vs0.81). Oncologist contouring time reduced from 17 to 24 min of manual contouring time to 3-7 min of editing time for the two observers (p < 0.01). Conclusion: Automatic contouring with a commercial AI-based system followed by editing can replace manual contouring, resulting in significantly reduced time for segmentation and better consistency between operators.

Limited impact of discretization/interpolation parameters on the predictive power of CT radiomic features in a surgical cohort of pancreatic cancer patients.

PURPOSE: To explore the variation of the discriminative power of CT (Computed Tomography) radiomic features (RF) against image discretization/interpolation in predicting early distant relapses (EDR) after upfront surgery. MATERIALS AND METHODS: Data of 144 patients with pre-surgical high contrast CT were processed consistently with IBSI (Image Biomarker Standardization Initiative) guidelines. Image interpolation/discretization parameters were intentionally changed, including cubic voxel size (0.21-27 mm3) and binning (32-128 grey levels) in a 15 parameter's sets. After excluding RF with poor inter-observer delineation agreement (ICC < 0.80) and not negligible inter-scanner variability, the variation of 80 RF against discretization/interpolation was first quantified. Then, their ability in classifying patients with early distant relapses (EDR, < 10 months, previously assessed at the first quartile value of time-to-relapse) was investigated in terms of AUC (Area Under Curve) variation for those RF significantly associated to EDR. RESULTS: Despite RF variability against discretization/interpolation parameters was large and only 30/80 RF showed %COV < 20 (%COV = 100*STDEV/MEAN), AUC changes were relatively limited: for 30 RF significantly associated with EDR (AUC values around 0.60-0.70), the mean values of SD of AUC variability and AUC range were 0.02 and 0.05 respectively. AUC ranges were between 0.00 and 0.11, with values ≤ 0.05 in 16/30 RF. These variations were further reduced when excluding the extreme values of 32 and 128 for grey levels (Average AUC range 0.04, with values between 0.00 and 0.08). CONCLUSIONS: The discriminative power of CT RF in the prediction of EDR after upfront surgery for pancreatic cancer is relatively invariant against image interpolation/discretization within a large range of voxel sizes and binning.

Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol.

PURPOSE: To extend the knowledge-based (KB) automatic planning approach to CyberKnife in the case of Stereotactic Body Radiation Therapy (SBRT) for prostate cancer. METHODS: Seventy-two clinical plans of patients treated according to the RTOG0938 protocol (36.25 Gy/5fr) with CyberKnife were exported from the CyberKnife system to Eclipse to train a KB-model using the Rapid Plan tool. The KB approach provided dose-volume objectives for specific OARs only and not PTV. Bladder, rectum and femoral heads were considered in the model. The KB-model was successfully trained on 51 plans and then validated on 20 new patients. A KB-based template was tuned in the Precision system for both sequential optimization (SO) and VOLO optimization algorithms. Plans of the validation group were re-optimized (KB-TP) using both algorithms without any operator intervention and compared against the original plans (TP) in terms of OARs/PTV dose-volume parameters. Paired Wilcoxon signed-rank tests were performed to assess statistically significant differences (p < 0.05). RESULTS: Regarding SO, automatic KB-TP plans were generally better than or equivalent to TP plans. PTVs V95% was slightly worse while OARs sparing for KB-TP was significantly improved. Regarding VOLO optimization, the PTVs coverage was significantly better for KB-TP while there was a limited worsening in the rectum. A significant improvement was observed in the bladder in the range of low-intermediate doses. CONCLUSIONS: An extension of the KB optimization approach to the CyberKnife system has been successfully developed and validated in the case of SBRT prostate cancer.

External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer.

PURPOSE/OBJECTIVE: The purpose of the study is to externally validate published 18F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy. MATERIAL/METHODS: Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66-69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (Fi), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score RF derived from Fi was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score RF were also tested on the entire dataset. RESULTS: Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62-0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6-11.4). The 5-year CSS for R were 97% (95% CI: 93-100%) vs 74% (56-92%) for low- and high-risk groups, respectively. RF and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (RF>0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89-100%) vs 65% (36-94%) and 97% (95% CI: 88-100%) vs 77% (58-93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group. CONCLUSION: A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy.

CT radiomic predictors of local relapse after SBRT for lung oligometastases from colorectal cancer: a single institute pilot study.

OBJECTIVES: To assess the potential of radiomic features (RFs) extracted from simulation computed tomography (CT) images in discriminating local progression (LP) after stereotactic body radiotherapy (SBRT) in the management of lung oligometastases (LOM) from colorectal cancer (CRC). MATERIALS AND METHODS: Thirty-eight patients with 70 LOM treated with SBRT were analyzed. The largest LOM was considered as most representative for each patient and was manually delineated by two blinded radiation oncologists. In all, 141 RFs were extracted from both contours according to IBSI (International Biomarker Standardization Initiative) recommendations. Based on the agreement between the two observers, 134/141 RFs were found to be robust against delineation (intraclass correlation coefficient [ICC] > 0.80); independent RFs were then assessed by Spearman correlation coefficients. The association between RFs and LP was assessed with Mann-Whitney test and univariate logistic regression (ULR): the discriminative power of the most informative RF was quantified by receiver-operating characteristics (ROC) analysis through area under curve (AUC). RESULTS: In all, 15/38 patients presented LP. Median time to progression was 14.6 months (range 2.4-66 months); 5/141 RFs were significantly associated to LP at ULR analysis (p < 0.05); among them, 4 RFs were selected as robust and independent: Statistical_Variance (AUC = 0.75, p = 0.002), Statistical_Range (AUC = 0.72, p = 0.013), Grey Level Size Zone Matrix (GLSZM) _zoneSizeNonUniformity (AUC = 0.70, p = 0.022), Grey Level Dependence Zone Matrix (GLDZM) _zoneDistanceEntropy (AUC = 0.70, p = 0.026). Importantly, the RF with the best performance (Statisical_Variance) is simply representative of density heterogeneity within LOM. CONCLUSION: Four RFs extracted from planning CT were significantly associated with LP of LOM from CRC treated with SBRT. Results encourage further research on a larger population aiming to define a usable radiomic score combining the most predictive RFs and, possibly, additional clinical features.

Radiation exposure during modern therapeutic endoscopic ultrasound procedures and standard alternatives.

Background and study aims Therapeutic EUS (t-EUS) is increasingly being adopted in clinical practice in tertiary referral centers; however, little is known about radiation exposure (RE) metrics and diagnostic reference limits for it. Methods Kerma-area product (KAP [Gy·cm 2 ]), Air Kerma and fluoroscopy time were retrospectively evaluated for all consecutive t-EUS procedures performed in San Raffaele Institute between 2019 and 2021. For EUS-guided choledochoduodenostomies (EUS-CDS) and gastroenterostomies (EUS-GE), an equal number of endoscopic retrograde cholangiopancreatographies (ERCPs) plus metal stenting and duodenal stents were included respectively for comparison. Results Data from 141 t-EUS procedures were retrieved (49 % pancreatic cancer, 38 % peripancreatic fluid collections). EUS-CDS (N = 44) were mainly performed fluoroless, while ERCPs required a significantly higher RE (KAP = 25 [17-55], P < 0.0001). Fluid collection drainage (EUS-FCD) with lumen apposing metal stents (LAMS, N = 26) were performed fluoroless, while EUS-FCD with double-pigtail plastic stents (DPPS, N = 28) required higher RE (KAP = 23 [13-45]). EUS-guided gallbladder drainage (EUS-GBD, N = 6) required scarce RE (KAP = 9 [3-21]) for coaxial DPPS placement. EUS-GE (N = 27) required higher RE than duodenal stenting (KAP = 44 [28-88] versus 29 [19-46], P = 0.03). EUS-guided hepaticogastrostomies (EUS-HGS, N = 10) had the highest RE among t-EUS procedures (KAP = 81 [49-123]). Procedure complexity or intervening complications were evaluated and resulted in higher RE within each procedure. Conclusions t-EUS procedures have different RE ( P  < 0.000001). EUS-CDS, EUS-GBD, and EUS-FCD with LAMS can be performed with no-to-mild radioscopy, unlike standard alternatives. However, radioscopy remains essential in case of technical difficulties or complications. EUS-GE and EUS-HGS involve a high RE. Endoscopists involved in t-EUS might experience RE higher than category standards, which indicates a need for increased awareness and personalized preventive measures.

Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy.

Background/Purpose: Tomotherapy may deliver high-quality whole breast irradiation at static angles. The aim of this study was to implement Knowledge-Based (KB) automatic planning for left-sided whole breast using this modality. Materials/Methods: Virtual volumetric plans were associated to the dose distributions of 69 Tomotherapy (TT) clinical plans of previously treated patients, aiming to train a KB-model using a commercial tool completely implemented in our treatment planning system. An individually optimized template based on the resulting KB-model was generated for automatic plan optimization. Thirty patients of the training set and ten new patients were considered for internal/external validation. Fully-automatic plans (KB-TT) were generated and compared using the same geometry/number of fields of the corresponding clinical plans. Results: KB-TT plans were successfully generated in 26/30 and 10/10 patients of the internal/external validation sets; for 4 patients whose original plans used only two fields, the manual insertion of one/two fields before running the automatic template was sufficient to obtain acceptable plans. Concerning internal validation, planning target volume V95%/D1%/dose distribution standard deviation improved by 0.9%/0.4Gy/0.2Gy (p < 0.05) against clinical plans; Organs at risk mean doses were also slightly improved (p < 0.05) by 0.07/0.4/0.2/0.01 Gy for left lung/heart/right breast/right lung respectively. Similarly satisfactory results were replicated in the external validation set. The resulting treatment duration was 8 ± 1 min, consistent with our clinical experience. The active planner time per patient was 5-10 minutes. Conclusion: Automatic TT left-sided breast KB-plans are comparable to or slightly better than clinical plans and can be obtained with limited planner time. The approach is currently under clinical implementation.

Atlas-based lung segmentation combined with automatic densitometry characterization in COVID-19 patients: Training, validation and first application in a longitudinal study.

PURPOSE: To develop and validate an automated segmentation tool for COVID-19 lung CTs. To combine it with densitometry information in identifying Aerated, Intermediate and Consolidated Volumes in admission (CT1) and follow up CT (CT3). MATERIALS AND METHODS: An Atlas was trained on manually segmented CT1 of 250 patients and validated on 10 CT1 of the training group, 10 new CT1 and 10 CT3, by comparing DICE index between automatic (AUTO), automatic-corrected (AUTOMAN) and manual (MAN) contours. A previously developed automatic method was applied on HU lung density histograms to quantify Aerated, Intermediate and Consolidated Volumes. Volumes of subregions in validation CT1 and CT3 were quantified for each method. RESULTS: In validation CT1/CT3, manual correction of automatic contours was not necessary in 40% of cases. Mean DICE values for both lungs were 0.94 for AUTOVsMAN and 0.96 for AUTOMANVsMAN. Differences between Aerated and Intermediate Volumes quantified with AUTOVsMAN contours were always < 6%. Consolidated Volumes showed larger differences (mean: -95 ± 72 cc). If considering AUTOMANVsMAN volumes, differences got further smaller for Aerated and Intermediate, and were drastically reduced for consolidated Volumes (mean: -36 ± 25 cc). The average time for manual correction of automatic lungs contours on CT1 was 5 ± 2 min. CONCLUSIONS: An Atlas for automatic segmentation of lungs in COVID-19 patients was developed and validated. Combined with a previously developed method for lung densitometry characterization, it provides a fast, operator-independent way to extract relevant quantitative parameters with minimal manual intervention.

Fabrication of 3D printed patient-derived anthropomorphic breast phantoms for mammography and digital breast tomosynthesis: Imaging assessment with clinical X-ray spectra.

PURPOSE: To design, fabricate and characterize 3D printed, anatomically realistic, compressed breast phantoms for digital mammography (DM) and digital breast tomosynthesis (DBT) x-ray imaging. MATERIALS: We realized 3D printed phantoms simulating healthy breasts, via fused deposition modeling (FDM), with a layer resolution of 0.1 mm and 100% infill density, using a dual extruder printer. The digital models were derived from a public dataset of segmented clinical breast computed tomography scans. Three physical phantoms were printed in polyethylene terephthalate (PET), acrylonitrile-butadiene-styrene (ABS), or in polylactic-acid (PLA) materials, using ABS as a substitute for adipose tissue, and PLA or PET filaments for replicating glandular and skin tissues. 3D printed phantoms were imaged at three clinical centers with DM and DBT scanners, using typical spectra. Anatomical noise of the manufactured phantoms was evaluated via the estimates of the ß parameter both in DM images and in images acquired via a clinical computed tomography (CT) scanner. RESULTS: DM and DBT phantom images showed an inner texture qualitatively similar to the images of a clinical DM or DBT exam, suitably reproducing the glandular structure of their computational phantoms. ß parameters evaluated in DM images of the manufactured phantoms ranged between 2.84 and 3.79; a lower ß was calculated from the CT scan. CONCLUSIONS: FDM 3D printed compressed breast phantoms have been fabricated using ABS, PLA and PET filaments. DM and DBT images with clinical x-ray spectra showed realistic textures. These phantoms appear promising for clinical applications in quality assurance, image quality and dosimetry assessments.

Stomach and duodenum dose-volume constraints for locally advanced pancreatic cancer patients treated in 15 fractions in combination with chemotherapy.

Purpose: To assess dosimetry predictors of gastric and duodenal toxicities for locally advanced pancreatic cancer (LAPC) patients treated with chemo-radiotherapy in 15 fractions. Methods: Data from 204 LAPC patients treated with induction+concurrent chemotherapy and radiotherapy (44.25 Gy in 15 fractions) were available. Forty-three patients received a simultaneous integrated boost of 48-58 Gy. Gastric/duodenal Common Terminology Criteria for Adverse Events v. 5 (CTCAEv5) Grade ≥2 toxicities were analyzed. Absolute/% duodenal and stomach dose-volume histograms (DVHs) of patients with/without toxicities were compared: the most predictive DVH points were identified, and their association with toxicity was tested in univariate and multivariate logistic regressions together with near-maximum dose (D0.03) and selected clinical variables. Results: Toxicity occurred in 18 patients: 3 duodenal (ulcer and duodenitis) and 10 gastric (ulcer and stomatitis); 5/18 experienced both. At univariate analysis, V44cc (duodenum: p = 0.02, OR = 1.07; stomach: p = 0.01, OR = 1.12) and D0.03 (p = 0.07, OR = 1.19; p = 0.008, OR = 1.12) were found to be the most predictive parameters. Stomach/duodenum V44Gy and stomach D0.03 were confirmed at multivariate analysis and found to be sufficiently robust at internal, bootstrap-based validation; the results regarding duodenum D0.03 were less robust. No clinical variables or %DVH was significantly associated with toxicity. The best duodenum cutoff values were V44Gy < 9.1 cc (and D0.03 < 47.6 Gy); concerning the stomach, they were V44Gy < 2 cc and D0.03 < 45 Gy. The identified predictors showed a high negative predictive value (>94%). Conclusion: In a large cohort treated with hypofractionated radiotherapy for LAPC, the risk of duodenal/gastric toxicities was associated with duodenum/stomach DVH. Constraining duodenum V44Gy < 9.1 cc, stomach V44Gy < 2 cc, and stomach D0.03 < 45 Gy should keep the toxicity rate at approximately or below 5%. The association with duodenum D0.03 was not sufficiently robust due to the limited number of events, although results suggest that a limit of 45-46 Gy should be safe.

Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach.

Despite careful selection, the recurrence rate after upfront surgery for pancreatic adenocarcinoma can be very high. We aimed to construct and validate a model for the prediction of early distant recurrence (<12 months from index surgery) after upfront pancreaticoduodenectomy. After exclusions, 147 patients were retrospectively enrolled. Preoperative clinical and radiological (CT-based) data were systematically evaluated; moreover, 182 radiomics features (RFs) were extracted. Most significant RFs were selected using minimum redundancy, robustness against delineation uncertainty and an original machine learning bootstrap-based method. Patients were split into training (n = 94) and validation cohort (n = 53). Multivariable Cox regression analysis was first applied on the training cohort; the resulting prognostic index was then tested in the validation cohort. Clinical (serum level of CA19.9), radiological (necrosis), and radiomic (SurfAreaToVolumeRatio) features were significantly associated with the early resurge of distant recurrence. The model combining these three variables performed well in the training cohort (p = 0.0015, HR = 3.58, 95%CI = 1.98-6.71) and was then confirmed in the validation cohort (p = 0.0178, HR = 5.06, 95%CI = 1.75-14.58). The comparison of survival curves between low and high-risk patients showed a p-value <0.0001. Our model may help to better define resectability status, thus providing an actual aid for pancreatic adenocarcinoma patients' management (upfront surgery vs. neoadjuvant chemotherapy). Independent validations are warranted.

Ten Year Results of Extensive Nodal Radiotherapy and Moderately Hypofractionated Simultaneous Integrated Boost in Unfavorable Intermediate-, High-, and Very High-Risk Prostate Cancer.

AIMS: To report 10-year outcomes of WPRT and HD moderately hypofractionated SIB to the prostate in UIR, HR, and VHR PCa. METHODS: From 11/2005 to 12/2015, 224 UIR, HR, and VHR PCa patients underwent WPRT at 51.8 Gy/28 fractions and SIB at 74.2 Gy (EQD2 88 Gy) to the prostate. Androgen deprivation therapy (ADT) was prescribed in up to 86.2% of patients. RESULTS: Median follow-up was 96.3 months (IQR: 71-124.7). Median age was 75 years (IQR: 71.3-78.1). At last follow up, G3 GI-GU toxicity was 3.1% and 8%, respectively. Ten-year biochemical relapse-free survival (bRFS) was 79.8% (95% CI: 72.3-88.1%), disease-free survival (DFS) 87.8% (95% CI: 81.7-94.3%), overall survival (OS) 65.7% (95% CI: 58.2-74.1%), and prostate cancer-specific survival (PCSS) 94.9% (95% CI: 91.0-99.0%). Only two patients presented local relapse. At univariate analysis, VHR vs. UIR was found to be a significant risk factor for biochemical relapse (HR: 2.8, 95% CI: 1.17-6.67, p = 0.021). After model selection, only Gleason Score ≥ 8 emerged as a significant factor for biochemical relapse (HR = 2.3, 95% CI: 1.12-4.9, p = 0.023). Previous TURP (HR = 3.5, 95% CI: 1.62-7.54, p = 0.001) and acute toxicity ≥ G2 (HR = 3.1, 95% CI = 1.45-6.52, p = 0.003) were significant risk factors for GU toxicity ≥ G3. Hypertension was a significant factor for GI toxicity ≥ G3 (HR = 3.63, 95% CI: 1.06-12.46, p = 0.041). ADT (HR = 0.31, 95% CI: 0.12-0.8, p = 0.015) and iPsa (HR = 0.37, 95% CI: 0.16-0.83, p = 0.0164) played a protective role. CONCLUSIONS: WPRT and HD SIB to the prostate combined with long-term ADT, in HR PCa, determine good outcomes with acceptable toxicity.

Replacing Manual Planning of Whole Breast Irradiation With Knowledge-Based Automatic Optimization by Virtual Tangential-Fields Arc Therapy.

PURPOSE: To implement Knowledge Based (KB) automatic planning for right and left-sided whole breast treatment through a new volumetric technique (ViTAT, Virtual Tangential-fields Arc Therapy) mimicking conventional tangential fields (TF) irradiation. MATERIALS AND METHOD: A total of 193 clinical plans delivering TF with wedged or field-in-field beams were selected to train two KB-models for right(R) and left(L) sided breast cancer patients using the RapidPlan (RP) tool implemented in the Varian Eclipse system. Then, a template for ViTAT optimization, incorporating individual KB-optimized constraints, was interactively fine-tuned. ViTAT plans consisted of four arcs (6 MV) with start/stop angles consistent with the TF geometry variability within our population; the delivery was completely blocked along the arcs, apart from the first and last 20° of rotation for each arc. Optimized fine-tuned KB templates for automatic plan optimization were generated. Validation tests were performed on 60 new patients equally divided in R and L breast treatment: KB automatic ViTAT-plans (KB-ViTAT) were compared against the original TF plans in terms of OARs/PTVs dose-volume parameters. Wilcoxon-tests were used to assess the statistically significant differences. RESULTS: KB models were successfully generated for both L and R sides. Overall, 1(3%) and 7(23%) out of 30 automatic KB-ViTAT plans were unacceptable compared to TF for R and L side, respectively. After the manual refinement of the start/stop angles, KB-ViTAT plans well fitted TF-performances for these patients as well. PTV coverage was comparable, while PTV D1% was improved with KB-ViTAT by R:0.4/L:0.2 Gy (p < 0.05); ipsilateral OARs Dmean were similar with a slight (i.e., few % volume) improvement/worsening in the 15-35 Gy/2-15 Gy range, respectively. KB-ViTAT better spared contralateral OARs: Dmean of contralateral OARs was 0.1 Gy lower (p < 0.05); integral dose was R:5%/L:8% lower (p < 0.05) than TF. The overall time for the automatic plan optimization and final dose calculation was 12 ± 2 minutes. CONCLUSIONS: Fully automatic KB-optimization of ViTAT can efficiently replace manually optimized TF planning for whole breast irradiation. This approach was clinically implemented in our institute and may be suggested as a large-scale strategy for efficiently replacing manual planning with large sparing of time, elimination of inter-planner variability and of, seldomly occurring, sub-optimal manual plans.

Decommissioning procedure and induced activation levels, calculations and measurements in an 18 MeV medical cyclotron.

The present article describes the decommissioning of a self-shielded 18 MeV medical cyclotron IBA Cyclone 18/9 after 14 years of operation. A Monte Carlo simulation of the possible nuclear reactions was performed in order to plan the decommissioning activities. During the cyclotron dismantling, the activities of the cyclotron components, concrete wall and floor samples were measured. Residual activities were analysed by means of an HPGe detector and liquid scintillation counting, and compared with simulation data. Dosimetry of the staff involved in the decommissioning procedure was monitored by individual TL dosimeters and/or digital dosimeter. The cyclotron component analysis confirmed the presence of gamma and pure beta emitters,22Na,54Mn,60Co,65Zn,207Bi,55Fe,63Ni at different values of specific activity, depending on the positioning of the sample point and on the alloy of the sampled part. In these components the presence of gamma and pure beta emitters was measured 5 years after the shutdown at levels far above clearance limits as defined by the 'Recommended radiological protection criteria for the recycling of metals from the dismantling of nuclear installations' (RP89) guidelines. The simulation, carried out by FLUKA Code (version 2020.0.5) on the cyclotron components, provided good agreement with measurements, with a maximum discrepancy of the same order as the uncertainties. Four engineers of the cyclotron maintenance staff were involved in the dismounting of the hottest components and rigging of the cyclotron in the deposit 6 months after shutdown and two engineers were involved during the drilling phase 3.5 years after shutdown. The measured dose from external exposure of the involved staff was lower than 100µSv person-1during the first phase and lower than 20µSv person-1during the final drilling phase. Measured doses from intake were negligible. In conclusion, the decommissioning of the 18 MeV cyclotron does not represent a risk for the involved staff, but, due to the presence of long-lived radioisotopes, the cyclotron components are to be treated as low level radioactive waste, and stored in an authorised storage area for at least 25 years after shutdown.

Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry.

PURPOSE: To train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry. METHODS: Two-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. "Aerated" (AV), "consolidated" (CV) and "intermediate" (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model's performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model's performances were tested in the validation group. RESULTS: Forty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82-0.93). All models showed good calibration (R2:0.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R2:0-70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64). CONCLUSIONS: Few automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration.