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.

Efficacy of stereotactic body radiotherapy and response prediction using artificial intelligence in oligometastatic gynaecologic cancer.

PURPOSE: We present a large real-world multicentric dataset of ovarian, uterine and cervical oligometastatic lesions treated with SBRT exploring efficacy and clinical outcomes. In addition, an exploratory machine learning analysis was performed. METHODS: A pooled analysis of gynecological oligometastases in terms of efficacy and clinical outcomes as well an exploratory machine learning model to predict the CR to SBRT were carried out. The CR rate following radiotherapy (RT) was the study main endpoint. The secondary endpoints included the 2-year actuarial LC, DMFS, PFS, and OS. RESULTS: 501 patients from 21 radiation oncology institutions with 846 gynecological metastases were analyzed, mainly ovarian (53.1%) and uterine metastases(32.1%).Multiple fraction radiotherapy was used in 762 metastases(90.1%).The most frequent schedule was 24 Gy in 3 fractions(13.4%). CR was observed in 538(63.7%) lesions. The Machine learning analysis showed a poor ability to find covariates strong enough to predict CR in the whole series. Analyzing them separately, in uterine cancer, if RT dose≥78.3Gy, the CR probability was 75.4%; if volume was <13.7 cc, the CR probability became 85.1%. In ovarian cancer, if the lesion was a lymph node, the CR probability was 71.4%; if volume was <17 cc, the CR probability rose to 78.4%. No covariate predicted the CR for cervical lesions. The overall 2-year actuarial LC was 79.2%, however it was 91.5% for CR and 52.5% for not CR lesions(p < 0.001). The overall 2-year DMFS, PFS and OS rate were 27.3%, 24.8% and 71.0%, with significant differences between CR and not CR. CONCLUSIONS: CR was substantially associated to patient outcomes in our series of gynecological cancer oligometastatic lesions. The ability to predict a CR through artificial intelligence could also drive treatment choices in the context of personalized oncology.

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.

Machine-learning prediction of treatment response to stereotactic body radiation therapy in oligometastatic gynecological cancer: A multi-institutional study.

BACKGROUND AND PURPOSE: We aimed to develop and validate different machine-learning (ML) prediction models for the complete response of oligometastatic gynecological cancer after SBRT. MATERIAL AND METHODS: One hundred fifty-seven patients with 272 lesions from 14 different institutions and treated with SBRT with radical intent were included. Thirteen datasets including 222 lesions were combined for model training and internal validation purposes, with an 80:20 ratio. The external testing dataset was selected as the fourteenth Institution with 50 lesions. Lesions that achieved complete response (CR) were defined as responders. Prognostic clinical and dosimetric variables were selected using the LASSO algorithm. Six supervised ML models, including logistic regression (LR), classification and regression tree analysis (CART) and support vector machine (SVM) using four different kernels, were trained and tested to predict the complete response of uterine lesions after SBRT. The performance of models was assessed by receiver operating characteristic curves (ROC), area under the curve (AUC) and calibration curves. An explainable approach based on SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanations of the model's decisions. RESULTS: 63.6% of lesions had a complete response and were used as ground truth for the supervised models. LASSO strongly associated complete response with three variables, namely the lesion volume (PTV), the type of lesions (lymph-nodal versus parenchymal), and the biological effective dose (BED10), that were used as input for ML modeling. In the training set, the AUCs for complete response were 0.751 (95% CI: 0.716-0.786), 0.766 (95% CI: 0.729-0.802) and 0.800 (95% CI: 0.742-0.857) for the LR, CART and SVM with a radial basis function kernel, respectively. These models achieve AUC values of 0.727 (95% CI: 0.669-0.795), 0.734 (95% CI: 0.649-0.815) and 0.771 (95% CI: 0.717-0.824) in the external testing set, demonstrating excellent generalizability. CONCLUSION: ML models enable a reliable prediction of the treatment response of oligometastatic lesions receiving SBRT. This approach may assist radiation oncologists to tailor more individualized treatment plans for oligometastatic 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.

Radiation Therapy for Gestational Trophoblastic Neoplasia: Forward-Looking Lessons Learnt.

Gestational trophoblastic neoplasia (GTN) includes several rare malignant diseases occurring after pregnancy: invasive moles, choriocarcinoma, placental site trophoblastic tumours, and epithelioid trophoblastic tumours. Multidisciplinary protocols including multi-agent chemotherapy, surgery, and occasionally radiotherapy achieve good outcomes for some high-risk metastatic patients. In this narrative review of the published studies on the topic, we have tried to identify the role of radiotherapy. The available studies are mainly small, old, and retrospective, with incomplete data regarding radiotherapy protocols delivering low doses (which can make this disease appear radioresistant in some cases despite high response rates with palliative doses) to wide fields (whole-brain, whole-liver, etc.), which can increase toxicity. Studies considering modern techniques are needed to overcome these limitations and determine the full potential of radiotherapy beyond its antihemorrhagic and palliative roles.

Efficacy and Safety of Stereotactic Body Radiation Therapy in Oligometastatic Uterine Cancer (MITO-RT2/RAD): A Large, Real-World Study in Collaboration With Italian Association of Radiation Oncology, Multicenter Italian Trials in Ovarian Cancer, and Mario Negri Gynecologic Oncology Group Groups.

PURPOSE: This retrospective, multicenter study analyzes the efficacy and safety of stereotactic body radiation therapy in a large cohort of patients with oligometastatic/persistent/recurrent uterine cancer. METHODS AND MATERIALS: Clinical and radiation therapy data from several radiation therapy centers treating patients by stereotactic body radiation therapy between March 2006 and October 2021 were collected. Objective response rate was defined as complete and partial response, and clinical benefit included objective response rate plus stable disease. Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer and Common Terminology Criteria for Adverse Events scales were used to grade toxicities. Primary endpoints were the rate of complete response to stereotactic body radiation therapy, and the 2-year actuarial local control rate "per-lesion" basis. Secondary endpoints were progression-free survival and overall survival, as well as toxicity. RESULTS: In the study, 157 patients with oligometastatic/persistent/recurrent uterine cancer bearing 272 lesions treated by stereotactic body radiation therapy at 14 centers were analyzed. Lymph node metastases (137, 50.4%) were prevalent, followed by parenchyma lesions (135, 49.6%). Median total dose was 35 Gy (10-75.2), in 5 fractions (range, 1-10). Complete and partial responses were 174 (64.0%), and 54 (19.9%), respectively. Stable disease was registered in 29 (10.6%), and 15 (5.5%) lesions progressed. Type of lesion (lymph node), volume (≤13.7 cc) and total dose (BED10 >59.5 Gy) were significantly associated with a higher probability of achieving complete response. Patients achieving complete response (CR) "per-lesion" basis experienced a 2-year actuarial local control rate of 92.4% versus 33.5% in lesions not achieving complete response (NCR; P < .001). Moreover, the 2-year actuarial progression-free survival rate in patients with CR was 45.4%, and patients with NCR had a 2-year rate of 17.6% (P < .001). Finally, patients who had a CR had a 2-year overall survival rate of 82.7%, compared with 56.5% for NCR patients (P <.001). Severe acute toxicity was around 2%, including one toxic death due to gastric perforation, and severe late toxicity around 4%. CONCLUSIONS: The efficacy of stereotactic body radiation therapy in this setting was confirmed. The low toxicity profile and the high local control rate in complete responder patients encourage the wider use of this approach.

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.

Salvage Stereotactic Reirradiation for Local Recurrence in the Prostatic Bed After Prostatectomy: A Retrospective Multicenter Study.

BACKGROUND: Management of local recurrence of prostate cancer (PCa) in the prostatic bed after radical prostatectomy (RP) and radiotherapy remains challenging. OBJECTIVE: To assess the efficacy and safety of salvage stereotactic body radiotherapy (SBRT) reirradiation in this setting and evaluate prognostic factors. DESIGN, SETTING, AND PARTICIPANTS: We conducted a large multicenter retrospective series that included 117 patients who were treated with salvage SBRT for local recurrence in the prostatic bed after RP and radiotherapy in 11 centers across three countries. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Progression-free survival (PFS; biochemical, clinical, or both) was estimated using the Kaplan-Meier method. Biochemical recurrence was defined as prostate-specific antigen nadir +0.2 ng/ml, confirmed by a second increasing measure. The cumulative incidence of late toxicities was estimated using the Kalbfleisch-Prentice method by considering recurrence or death as a competing event. RESULTS AND LIMITATIONS: The median follow-up was 19.5 mo. The median SBRT dose was 35 Gy. The median PFS was 23.5 mo (95% confidence interval [95% CI], 17.6-33.2). In the multivariable models, the volume of the recurrence and its contact with the urethrovesical anastomosis were significantly associated with PFS (hazard ratio [HR]/10 cm3 = 1.46; 95% CI, 1.08-1.96; p = 0.01 and HR = 3.35; 95% CI, 1.38-8.16; p = 0.008, respectively). The 3-yr cumulative incidence of grade ≥2 late GU or GI toxicity was 18% (95% CI, 10-26). In the multivariable analysis, a recurrence in contact with the urethrovesical anastomosis and D2% of the bladder were significantly associated with late toxicities of any grade (HR = 3.65; 95% CI, 1.61-8.24; p = 0.002 and HR/10 Gy = 1.88; 95% CI, 1.12-3.16; p = 0.02, respectively). CONCLUSIONS: Salvage SBRT for local recurrence in the prostate bed may offer encouraging control and acceptable toxicity. Therefore, further prospective studies are warranted. PATIENT SUMMARY: We found that salvage stereotactic body radiotherapy after surgery and radiotherapy allows for encouraging control and acceptable toxicity in locally relapsed prostate cancer.

A predictive model of polymetastatic disease from a multicenter large retrospectIve database on colorectal lung metastases treated with stereotactic ablative radiotherapy: The RED LaIT-SABR study.

Aim: Stereotactic ablative radiotherapy (SABR) showed increasing survival in oligometastatic patients. Few studies actually depicted oligometastatic disease (OMD) evolution and which patient will remain disease-free and which will rapidly develop a polymetastatic disease (PMD) after SABR. Therefore, apart from the number of active metastases, there are no clues on which proven factor should be considered for prescribing local treatment in OMD. The study aims to identify predictive factors of polymetastatic evolution in lung oligometastatic colorectal cancer patients. Methods: This international Ethical Committee approved trial (Prot. Negrar 2019-ZT) involved 23 Centers and 450 lung oligometastatic patients. Primary end-point was time to the polymetastatic conversion (tPMC). Additionally, oligometastases number and cumulative gross tumor volume (cumGTV) were used as combined predictive factors of tPMC. Oligometastases number was stratified as 1, 2-3, and 4-5; cumGTV was dichotomized to the value of 10 cc. Results: The median tPMC in the overall population was 26 months. Population was classified in the following tPMC risk classes: low-risk (1-3 oligometastases and cumGTV ≤ 10 cc) with median tPMC of 35.1 months; intermediate-risk (1-3 oligometastases and cumGTV > 10 cc), with median tPMC of 13.9 months, and high-risk (4-5 oligometastases, any cumGTV) with median tPMC of 9.4 months (p = 0.000). Conclusion: The present study identified predictive factors of polymetastatic evolution after SABR in lung oligometastatic colorectal cancer. The results demonstrated that the sole metastases number is not sufficient to define the OMD since patients defined oligometastatic from a numerical point of view might rapidly progress to PMD when the cumulative tumor volume is high. A tailored approach in SABR prescription should be pursued considering the expected disease evolution after SABR, with the aim to avoid unnecessary treatment and toxicity in those at high risk of polymetastatic spread, and maximize local treatment in those with a favorable disease evolution.

Granulosa cell tumors (GCTs) of the ovary: What is the role of radiotherapy?

Granulosa cell tumors of the ovary have an indolent behavior and a good prognosis, but a high incidence of local recurrence after surgery. The best treatment in the recurrent setting is unclear and randomized clinical trials on the management in the recurrent setting are lacking. The role of radiotherapy is controversial in adjuvant settings and unknown in case of relapse after surgery. This review aims to summarize the level of evidence of the role of radiation treatments for granulosa cell tumors of the ovary.

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.

Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse.

The detection of prostate cancer recurrence after external beam radiotherapy relies on the measurement of a sustained rise of serum prostate-specific antigen (PSA). However, this biochemical relapse may take years to occur, thereby delaying the delivery of a secondary treatment to patients with recurring tumors. To address this issue, we propose to use patient-specific forecasts of PSA dynamics to predict biochemical relapse earlier. Our forecasts are based on a mechanistic model of prostate cancer response to external beam radiotherapy, which is fit to patient-specific PSA data collected during standard posttreatment monitoring. Our results show a remarkable performance of our model in recapitulating the observed changes in PSA and yielding short-term predictions over approximately 1 year (cohort median root mean squared error of 0.10-0.47 ng/mL and 0.13 to 1.39 ng/mL, respectively). Additionally, we identify 3 model-based biomarkers that enable accurate identification of biochemical relapse (area under the receiver operating characteristic curve > 0.80) significantly earlier than standard practice (p < 0.01).

Accurate prediction of long-term risk of biochemical failure after salvage radiotherapy including the impact of pelvic node irradiation.

BACKGROUND AND PURPOSE: Explainable models of long-term risk of biochemical failure (BF) after post-prostatectomy salvage radiotherapy (SRT) are lacking. A previously introduced radiobiology-based formula was adapted to incorporate the impact of pelvic nodes irradiation (PNI). MATERIALS AND METHODS: The risk of post-SRT BF may be expressed by a Poisson-based equation including pre-SRT PSA, the radiosensitivity α, the clonogen density C, the prescribed dose (in terms of EQD2, α/ß = 1.5 Gy) and a factor (1-BxλxPSA) accounting for clonogens outside the irradiated volume, being λ the recovery due to PNI. Data of 795 pT2-pT3, pN0/pN1/pNx (n = 627/94/74) patients with follow-up ≥ 5 years and pre-RT PSA ≤ 2 ng/mL were randomly split into training (n = 528) and validation (n = 267) cohorts; the training cohort data were fitted by the least square method. Separate fits were performed for different risk groups. Model performances were assessed by calibration plots and tested in the validation group. RESULTS: The median follow-up was 8.5y, median pre-SRT PSA and EQD2 were 0.43 ng/mL and 71.3 Gy respectively; 331/795 pts received PNI. The most clinically significant prognostic grouping was pT3b and/or ISUP4-5 versus pT2/3a and ISUP1-3. Best-fit parameters were αeff = 0.26/0.23 Gy-1, C = 107/107, B = 0.40/0.97, λ = 0.87/0.41 for low/high-risk group. Performances were confirmed in the validation group (slope = 0.89,R2 = 0.85). Results suggested optimal SRT dose at 70-74 Gy. The estimated reduction of post-SRT BF due to PNI at these dose values was > 5 % for PSA > 1/>0.15 ng/mL for low/high-risk patients, being > 10 % for high-risk patients with pre-SRT PSA > 0.25 ng/mL. CONCLUSION: An explainable one-size-fits-all equation satisfactorily predicts long-term risk of post-SRT BF. The model was independently validated. A calculator tool was made available.

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.

Stereotactic body radiotherapy in oligometastatic cervical cancer (MITO-RT2/RAD study): a collaboration of MITO, AIRO GYN, and MaNGO groups.

OBJECTIVE: This retrospective, multicenter study analyzes the efficacy and safety of stereotactic body radiotherapy in a large cohort of patients with oligometastatic/persistent/recurrent cervical cancer. METHODS: A standardized data collection from several radiotherapy centers that treated patients by stereotactic body radiotherapy between March 2006 and February 2021 was set up. Clinical and stereotactic body radiotherapy parameters were collected. Objective response rate was defined as a composite of complete and partial response, while clinical benefit included objective response rate plus stable disease. Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer and Common Terminology Criteria for Adverse Events scales were used to grade toxicities. The primary endpoints were the rate of complete response to stereotactic body radiotherapy, and the 2 year actuarial local control rate on a 'per lesion' basis. The secondary end points were progression-free survival and overall survival, as well as toxicity. RESULTS: A total of 83 patients with oligometastatic/persistent/recurrent cervical cancer bearing 125 lesions treated by stereotactic body radiotherapy at 15 different centers were selected for analysis. Of the sites of metastatic disease, lymph node metastases were most common (55.2%), followed by parenchyma lesions (44.8%). Median total dose was 35 Gy (range 10-60), in five fractions (range 1-10), with a median dose/fraction of 7 Gy (range 4-26). Complete, partial, and stable response were found in 73 (58.4%), 29 (23.2%), and 16 (12.8%) lesions, respectively, reaching 94.4% of the clinical benefit rate. Forty-six (55.4%) patients had a complete response. Patients achieving complete response on a 'per lesion' basis experienced a 2 year actuarial local control rate of 89.0% versus 22.1% in lesions not achieving complete response (p<0.001). The 2 year actuarial progression-free survival rate was 42.5% in patients with complete response versus 7.8% in patients with partial response or stable or progressive disease (p=0.001). The 2 year actuarial overall survival rate was 68.9% in patients with complete response versus 44.3% in patients with partial response or stable or progressive disease (p=0.015). Fifteen patients (18.1%) had mild acute toxicity, totaling 29 side events. Late toxicity was documented in four patients (4.8%) totaling seven adverse events. CONCLUSION: Our analysis confirmed the efficacy of stereotactic body radiotherapy in oligometastatic/persistent/recurrent cervical cancer patients. The low toxicity profile encourages the wider use of stereotactic body radiotherapy in this setting.

Radiotherapy at oligoprogression for metastatic castration-resistant prostate cancer patients: a multi-institutional analysis.

PURPOSE: To retrospectively estimate the impact of radiotherapy as a progression-directed therapy (PDT) in oligoprogressive metastatic castration-resistant prostate cancer (mCRPC) patients under androgen receptor-target therapy (ARTT). MATERIALS AND METHODS: mCRPC patients are treated with PDT. End-points were time to next-line systemic treatment (NEST), radiological progression-free survival (r-PFS) and overall survival (OS). Toxicity was registered according to Common Terminology Criteria for Adverse Events v4.0. Survival analysis was performed using the Kaplan-Meier method; univariate and multivariate analyses were performed. RESULTS: Fifty-seven patients were analyzed. The median follow-up after PDT was 25.2 months (interquartile, 17.1-44.5). One-year NEST-free survival, r-PFS and OS were 49.8%, 50.4% and 82.1%, respectively. At multivariate analysis, polymetastatic condition at diagnosis of metastatic hormone-sensitive prostate cancer (mHSPC) (HR 2.82, p = 0.004) and PSA doubling time at diagnosis of mCRPC (HR 2.76, p = 0.006) were associated with NEST-free survival. The same variables were associated with r-PFS (HR 2.32, p = 0.021; HR 2.24, p = 0.021). One patient developed late grade ≥ 2 toxicity. CONCLUSION: Our study shows that radiotherapy in oligoprogressive mCRPC is safe, is effective and seems to prolong the efficacy of ARTT in patients who otherwise would have gone systemic treatment switch, positively affecting disease progression. Prospective trials are needed.