Hypofractionated radiotherapy concomitant to capecitabine after induction chemotherapy for advanced pancreatic adenocarcinoma.

Background and purpose: To assess feasibility, toxicity and outcome of moderately hypofractionated radiotherapy concomitant to capecitabine after induction chemotherapy for advanced pancreatic cancer. Materials and methods: Patients with advanced pancreatic cancer without distant progression after induction chemotherapy (CHT) were considered. Radiochemotherapy (RCT) consisted of 44.25 Gy in 15 fractions to the tumor and involved lymph-nodes concomitant to capecitabine 1250 mg/m2/day. Feasibility and toxicity were evaluated in all pts. Overall survival (OS), progression free survival (PFS), distant PFS (DPFS) and local PFS (LPFS) were assessed only in stage III patients. Results: 254 patients, 220 stage III, 34 stage IV, were treated. Median follow up was 19 months. Induction CHT consisted of Gemcitabine (35 patients), or drug combination (219 patients); median duration was 6 months.Four patients (1.6 %) did not complete RT (1 early progression, 3 toxicity), median duration of RT was 20 days, 209 patients (82 %) received ≥ 75 % of capecitabine dose.During RCT G3 gastrointestinal toxicity occurred in 3.2% of patients, G3-G4 hematologic toxicity in 5.4% of patients. Subsequently, G3, G4, G5 gastric or duodenal lesions occurred in 10 (4%), 2 (0.8%) and 1 patients (0.4%), respectively.Median PFS, LPFS, and DPFS were 11.9 months (95 % CI:11.4-13), 16 months (95 % CI:14.2-17.3) and 14.0 months (95 % CI:12.6-146.5), respectively.Median OS was 19.5 months (95 % CL:18.1-21.3). One- and two-year survival were 85.2 % and 36 %, respectively. Conclusions: The present schedule of hypofractionated RT after induction CHT is feasible with acceptable toxicity rate and provides an outcome comparable with that achievable with standard doses and fractionation.

Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study.

PURPOSE: The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT. METHODS: Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves. RESULTS: A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set). CONCLUSION: The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.

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.

Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization.

Background and Purpose: The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation. Materials and Methods: Forty-five previously treated patients (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan). Results: The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3-5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage. Conclusions: Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.

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.

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.

Radioluminescence imaging feasibility for robotic radiosurgery field size quality assurance.

PURPOSE: To investigate the feasibility of radioluminescence imaging (RLI) as a novel 2D quality assurance (QA) dosimetry system for CyberKnife®. METHODS: We developed a field size measurement system based on a commercial complementary metal oxide semiconductor (CMOS) camera facing a radioluminescence screen located at the isocenter normal to the beam axis. The radioluminescence light collected by a lens was used to measure 2D dose distributions. An image transformation procedure, based on two reference phantoms, was developed to correct for projective distortion due to the angle (15°) between the optical and beam axis. Dose profiles were measured for field sizes ranging from 5 mm to 60 mm using fixed circular and iris collimators and compared against gafchromic (GC) film. The corresponding full width at half maximum (FWHM) was measured using RLI and benchmarked against GC film. A small shift in the source-to-surface distance (SSD) of the measurement plane was intentionally introduced to test the sensitivity of the RLI system to field size variations. To assess reproducibility, the entire RLI procedure was tested by acquiring the 60 mm circle field three times on two consecutive days. RESULTS: The implemented procedure for perspective image distortion correction showed improvements of up to 1 mm using the star phantom against the square phantom. The FWHM measurements using the RLI system indicated a strong agreement with GC film with maximum absolute difference equal to 0.131 mm for fixed collimators and 0.056 mm for the iris. A 2D analysis of RLI with respect to GC film showed that the differences in the central region are negligible, while small discrepancies are in the penumbra region. Changes in field sizes of 0.2 mm were detectable by RLI. Repeatability measurements of the beam FWHM have shown a standard deviation equal to 0.11 mm. CONCLUSIONS: The first application of a RLI approach for CyberKnife® field size measurement was presented and tested. Results are in agreement with GC film measurements. Spatial resolution and immediate availability of the data indicate that RLI is a feasible technique for robotic radiosurgery QA.

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.

Evaluation of early regression index as response predictor in cervical cancer: A retrospective study on T2 and DWI MR images.

BACKGROUND AND PURPOSE: Early Regression Index (ERITCP) is an image-based parameter based on tumor control probability modelling, that reported interesting results in predicting pathological complete response (pCR) after pre-operative chemoradiotherapy (CRT) in rectal cancer. This study aims to evaluate this parameter for Locally Advanced Cervical Cancer (LACC), considering not only T2-weighted but also diffusion-weighted (DW) Magnetic Resonance (MR) images, comparing it with other image-based parameters such as tumor volumes and apparent coefficient diffusion (ADC). MATERIALS AND METHODS: A total of 88 patients affected by LACC (FIGO IB2-IVA) and treated with CRT were enrolled. An MRI protocol consisting in two acquisitions (T2-w and DWI) in two times (before treatment and at mid-therapy) was applied. Gross Tumor Volume (GTV) was delineated and ERITCP was calculated for both imaging modalities. Surgery was performed for each patient after nCRT: pCR was considered in case of absence of any residual tumor cells. The predictive performance of ERITCP, GTV volumes (calculated on T2-w and DW MR images) and ADC parameters were evaluated in terms of area (AUC) under the Receiver Operating Characteristic (ROC) curve considering pCR and two-years survival parameters as clinical outcomes. RESULTS: ERITCP and GTV volumes calculated on DW MR images (ERIDWI and Vmid_DWI) significantly predict pCR (AUC = 0.77 and 0.75 respectively) with results superior to those observed considering T2-w MR images or ADC parameters. Significance was also reported in the prediction of 2-years local control and disease free-survival. CONCLUSION: This study identified ERITCP and Vmid as good predictor of pCR in case of LACC, especially if calculated considering DWI. Using these indicators, it is possible to early identify not responders and modifying the treatment, accordingly.

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.

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.

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.

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.

Clinical Implementation of Knowledge-Based Automatic Plan Optimization for Helical Tomotherapy.

PURPOSE: To implement knowledge-based (KB) automatic planning for helical TomoTherapy (HTT). The focus of the first clinical implementation was the case of high-risk prostate cancer, including pelvic node irradiation. METHODS AND MATERIALS: One hundred two HTT clinical plans were selected to train a KB model using the RapidPlan tool incorporated in the Eclipse system (v13.6, Varian Inc). The individually optimized KB-based templates were converted into HTT-like templates and sent automatically to the HTT treatment planning system through scripting. The full dose calculation was set after 300 iterations without any additional planner intervention. Internal (20 patients in the training cohort) and external (28 new patients) validation were performed to assess the performance of the model: Automatic HTT plans (KB-TP) were compared against the original plans (TP) in terms of organs at risk and planning target volume (PTV) dose-volume parameters and by blinded clinical evaluation of 3 expert clinicians. RESULTS: KB-TP plans were generally better than or equivalent to TP plans in both validation cohorts. A significant improvement in PTVs and rectum-PTV overlap dosimetry parameters were observed for both sets. Organ-at-risk sparing for KB-TP was slightly improved, which was more evident in the external validation group and for bladder and bowel. Clinical evaluation reported KB-TP to be better in 60% of cases and worse in 10% compared with TP (P < .05). CONCLUSIONS: The fully KB-based automatic planning workflow was successfully implemented for HTT planning optimization in the case of high-risk patients with prostate cancer.

Impact of molecular subtype on 1325 early-stage breast cancer patients homogeneously treated with hypofractionated radiotherapy without boost: Should the indications for radiotherapy be more personalized?

AIM: We report molecular subtype impact on 1325 early breast cancer (BCa) patients treated with whole breast hypofractionated (WBH) adjuvant forward-planned intensity modulated radiotherapy (F-IMRT) without boost. METHODS AND MATERIALS: From 02/2009-05/2017 1325 patients with pTis-pT3, pNx-N1aM0 BCa who underwent breast conservation surgery were treated with WBHF-IMRT in our institute, to a total dose of 40 Gy/15 fractions, without boost. Median age: 62 (interquartile range-IQR-:51.14-70.53) years. HISTOLOGY: 8% in situ carcinoma (ISC), 92% invasive tumors. Molecular subtypes (invasive tumors): 49.9% Luminal A, 33.1% Luminal B Her2 negative (-), 6.2% Luminal B Her2 positive (+), 3.6% Hormone Receptor (HR)- Her2+, 7.1% Triple negative (TNBC), and 0.2% HR+. Chemotherapy (CT) was prescribed in 28% of patients, hormonal therapy in 80.3%, monoclonal antibodies (MAb) in 86.8% of Luminal B Her2+ and 97.7% of HR- Her2+ patients. RESULTS: Median follow up was 72.43 (IQR: 44.63-104.13) months. The 5-year Kaplan-Meier estimates of local relapse-free survival (LRFS) was 97.8%, regional-(RRFS) 98.6%, loco-regional- (LRRFS) 96.9%, distant- (DRFS) 96.6%, disease-free survival (DFS) 94.8% and overall survival (OS) 95.5%. Considering molecular subtypes, 5-year LRFS was: 99.8% for Luminal A, 96.7% for Luminal B Her2-, 94.1% for Luminal B Her2+, 87.9% for HR- Her2+, 95.1% for TNBC and 99.1% for in situ carcinoma. CONCLUSION: While the overall estimated probability of LR within 5 years after WBHF-IMRT without boost is good (2.2%), molecular subtypes have a strong impact, despite MAb therapy in Her2+ patients, and CT for TNBC patients, and could be used as a parameter in deciding the boost prescription.

External Validation of Early Regression Index (ERITCP) as Predictor of Pathologic Complete Response in Rectal Cancer Using Magnetic Resonance-Guided Radiation Therapy.

PURPOSE: Tumor control probability (TCP)-based early regression index (ERITCP) is a radiobiological parameter that showed promising results in predicting pathologic complete response (pCR) on T2-weighted 1.5 T magnetic resonance (MR) images of patients with locally advanced rectal cancer. This study aims to validate the ERITCP in the context of low-tesla MR-guided radiation therapy, using images acquired with different magnetic field strength (0.35 T) and image contrast (T2/T1). Furthermore, the optimal timing for pCR prediction was estimated, calculating the ERI index at different biologically effective dose (BED) levels. METHODS AND MATERIALS: Fifty-two patients with locally advanced rectal cancer treated with neoadjuvant chemoradiation therapy were enrolled in this multi-institutional retrospective study. For each patient, a 0.35 T T2/T1-weighted MR image was acquired during simulation and on each treatment day. Gross tumor volume was contoured according to International Commission on Radiation Units Report 83 guidelines. According to the original definition, ERITCP was calculated considering the residual tumor volume at BED = 25 Gy. ERI was also calculated in correspondence with several BED levels: 13, 21, 32, 40, 46, 54, 59, and 67. The predictive performance of the different ERI indices were evaluated in terms of receiver operating characteristic curve. The robustness of ERITCP with respect to the interobserver variability was also evaluated considering 2 operators and calculating the intraclass correlation index. RESULTS: Fourteen patients showed pCR. ERITCP correctly 47 of 52 cases (accuracy = 90%), showing good results in terms of sensitivity (86%), specificity (92%), negative predictive value (95%), and positive predictive value (80%). The analysis at different BED levels shows that the best predictive performance is obtained when this parameter is calculated at BED = 25 Gy (area under the curve = 0.93). ERITCP results are robust with respect to interobserver variability (intraclass correlation index = 0.99). CONCLUSIONS: This study confirmed the validity and the robustness of ERITCP as a pCR predictor in the context of low-tesla MR-guided radiation therapy and indicate 25 Gy as the best BED level to perform predictions.

Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms.

PURPOSE: To explore the variation of the discriminative power of CT radiomic features (RF) against image discretization/interpolation in characterizing pancreatic neuro-endocrine (PanNEN) neoplasms. MATERIALS AND METHODS: Thirty-nine PanNEN patients with pre-surgical high contrast CT available were considered. Image interpolation and discretization parameters were intentionally changed, including pixel size (0.73-2.19 mm2), slice thickness (2-5 mm) and binning (32-128 grey levels) and their combination generated 27 parameter's set. The ability of 69 RF in discriminating post-surgically assessed tumor grade (>G1), positive nodes, metastases and vascular invasion was tested: AUC changes when changing the parameters were quantified for selected RF, significantly associated to each end-point. The analysis was repeated for the corresponding images with contrast medium and in a sub-group of 29/39 patients scanned on a single scanner. RESULTS: The median tumor volume was 1.57 cm3 (16%-84% percentiles: 0.62-34.58 cm3). RF variability against discretization/interpolation parameters was large: only 21/69 RF showed %COV < 20%. Despite this variability, AUC changes were limited for all end-points: with typical AUC values around 0.75-0.85, AUC ranges for the 27 parameter's set were on average 0.062 (1SD:0.037) for all end-points with maximum %COV equal to 5.5% (mean:2.3%). Performances significantly improved when excluding the 5 mm thickness case and fixing the binning to 64 (mean AUC range: 0.036, 1SD:0.019). Using contrast images or limiting the population to single-scanner patients had limited impact on AUC variability. CONCLUSIONS: The discriminative power of CT RF for panNEN is relatively invariant against image interpolation/discretization within a large range of voxel sizes and binning.

Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer.

PURPOSE: To assess the value of 18F-Fluorodeoxyglucose (18F-FDG) PET Radiomic Features (RF) in predicting Distant Relapse Free Survival (DRFS) in patients with Locally AdvancedPancreaticCancer (LAPC) treated with radio-chemotherapy. MATERIALS & METHODS: One-hundred-ninety-eight RFs were extracted using IBSI (Image Biomarker Standardization Initiative) consistent software from pre-radiotherapy images of 176 LAPC patients treated with moderate hypo-fractionation (44.25 Gy, 2.95 Gy/fr). Tumors were segmented by applying a previously validated semi-automatic method. One-hundred-twenty-six RFs were excluded due to poor reproducibility and/or repeatability and/or inter-scanner variability. The original cohort was randomly split into a training (n = 116) and a validation (n = 60) group. Multi-variable Cox regression was applied to the training group, including only independent RFs in the model. The resulting radiomic index was tested in the validation cohort. The impact of selected clinical variables was also investigated. RESULTS: The resulting Cox model included two first order RFs: Center of Mass Shift (COMshift) and 10th Intensity percentile (P10) (p = 0.0005, HR = 2.72, 95%CI = 1.54-4.80), showing worse outcomes for patients with lower COMshift and higher P10. Once stratified by quartile values (highest quartile vs the remaining), the index properly stratified patients according to their DRFS (p = 0.0024, log-rank test). Performances were confirmed in the validation cohort (p = 0.03, HR = 2.53, 95%CI = 0.96-6.65). The addition of clinical factors did not significantly improve the models' performance. CONCLUSIONS: A radiomic-based index including only two robust PET-RFs predicted DRFS of LAPC patients after radio-chemotherapy. The current results could find relevant applications in the treatment personalization of LAPC. A multi-institution independent validation has been planned.

Predicting pathological response after radio-chemotherapy for rectal cancer: Impact of late oxaliplatin administration.

BACKGROUND AND PURPOSE: A previously introduced index based on early tumor (GTV) regression (ERITCP) during neo-adjuvant radio-chemotherapy of rectal cancer was used to investigate the impact of changes of oxaliplatin (OXA) delivery on the prediction of pathological complete response (pCR) and residual vital cell (RVC) fraction. MATERIALS AND METHODS: Ninety-five patients were treated following an adaptive protocol (41.4 Gy/18fr; 2.3 Gy/fr) delivering a simultaneous integrated boost to the residual GTV in the last 6 fractions (3 Gy/fr). OXA was delivered on days -14, 0 (start of RT) and +14. Based on the oncologist's preference, the last OXA cycle was not administered for 36 patients. MRIs taken at planning and at mid-RT were used to calculate ERITCP, before the timing of the third OXA cycle. The impact of OXA cycles and the discriminative power of ERITCP in predicting the pathological response (pCR, RVC >10%) were quantified. Multivariate logistic regression was performed to assess predictive models. RESULTS: Two patients with complete clinical remission refused surgery (cCR_ww). Complete post-surgical data of 54/59 and 35/36 patients were available for the two groups (3 vs 2 OXA cycles). pCR/pCR + cCR_ww/RVC >10% rates were 31.5/33.9/27.8% and 14.3/14.3/54.3% respectively (p = 0.01-0.07). ERITCP showed high negative predictive value (85-91%) for all end-points. The logistic predictive model for pCR included ERITCP (OR: 0.93) and OXA cycles (OR: 3.5), with AUC = 0.78. Internal validation through bootstrap confirmed the robustness of the results. CONCLUSIONS: Late omission of OXA dramatically reduced the pathological response. OXA delivery after the assessment of ERITCP significantly influenced the relationship between ERITCP and pCR.