Tailoring neoadjuvant systemic therapy in breast cancer: "The advent of a personalized approach"-The Breast-Gynecological and Immuno-Oncology International Cancer Conference (BGICC) consensus and recommendations.

BACKGROUND: The management of early breast cancer (BC) has witnessed an uprise in the use of neoadjuvant therapy and a remarkable reshaping of the systemic therapy postneoadjuvant treatment in the last few years, with the evolution of many controversial clinical situations that require consensus. METHODS: During the 14th Breast-Gynecological and Immuno-Oncology International Cancer Conference held in Egypt in 2022, a panel of 44 BC experts from 13 countries voted on statements concerning debatable challenges in the neo/adjuvant treatment setting. The recommendations were subsequently updated based on the most recent data emerging. A modified Delphi approach was used to develop this consensus. A consensus was achieved when ≥75% of voters selected an answer. RESULTS AND CONCLUSIONS: The consensus recommendations addressed different escalation and de-escalation strategies in the setting of neoadjuvant therapy for early BC. The recommendations recapitulate the available clinical evidence and expert opinion to individualize patient management and optimize therapy outcomes. Consensus was reached in 63% of the statements (52/83), and the rationale behind each statement was clarified.

A multicenter high-quality data registry for advanced proton therapy approaches: the POWER registry.

BACKGROUND: Paucity and low evidence-level data on proton therapy (PT) represent one of the main issues for the establishment of solid indications in the PT setting. Aim of the present registry, the POWER registry, is to provide a tool for systematic, prospective, harmonized, and multidimensional high-quality data collection to promote knowledge in the field of PT with a particular focus on the use of hypofractionation. METHODS: All patients with any type of oncologic disease (benign and malignant disease) eligible for PT at the European Institute of Oncology (IEO), Milan, Italy, will be included in the present registry. Three levels of data collection will be implemented: Level (1) clinical research (patients outcome and toxicity, quality of life, and cost/effectiveness analysis); Level (2) radiological and radiobiological research (radiomic and dosiomic analysis, as well as biological modeling); Level (3) biological and translational research (biological biomarkers and genomic data analysis). Endpoints and outcome measures of hypofractionation schedules will be evaluated in terms of either Treatment Efficacy (tumor response rate, time to progression/percentages of survivors/median survival, clinical, biological, and radiological biomarkers changes, identified as surrogate endpoints of cancer survival/response to treatment) and Toxicity. The study protocol has been approved by the IEO Ethical Committee (IEO 1885). Other than patients treated at IEO, additional PT facilities (equipped with Proteus®ONE or Proteus®PLUS technologies by IBA, Ion Beam Applications, Louvain-la-Neuve, Belgium) are planned to join the registry data collection. Moreover, the registry will be also fully integrated into international PT data collection networks.

Extreme-hypofractionated RT with concomitant boost to the DIL in PCa: a 5-year update on oncological and patient-reported outcomes for the phase II trial "GIVE ME FIVE".

AIM: The present work reports updated oncological results and patients-reported outcomes at 5 years of phase II trial "Short-term high precision RT for early prostate cancer with SIB to the dominant intraprostatic lesion (DIL) for patients with early-stage PCa". METHODS: Data from patients enrolled within AIRC IG-13218 (NCT01913717) trial were analyzed. Clinical and GU/GI toxicity assessment and PSA measurements were performed every 3 months for at least 2 years after RT end. QoL of enrolled patients was assessed by IPSS, EORTC QLQ-C30, EORTC QLQ-PR25, and IIEF-5. Patients' score changes were calculated at the end of RT and at 1, 12, and 60 months after RT. RESULTS: A total of 65 patients were included. At a median follow-up of 5 years, OS resulted 86%. Biochemical and clinical progression-free survival at 5 years were 95%. The median PSA at baseline was 6.07 ng/ml, while at last follow-up resulted 0.25 ng/ml. IPSS showed a statistically significant variation in urinary function from baseline (p = 0.002), with the most relevant deterioration 1 month after RT, with a recovery toward baseline at 12 months (p ≤ 0.0001). A numerical improvement in QoL according to the EORTC QLQ-C30 has been reported although not statistically significant. No change in sexual activity was recorded after RT. CONCLUSIONS: The study confirms that extreme hypofractionation with a DIL boost is safe and effective, with no severe effects on the QoL. The increasing dose to the DIL does not worsen the RT toxicity, thus opening the possibility of an even more escalated treatment.

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.

OBJECTIVE: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort. METHODS: Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow. RESULTS: The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging. CONCLUSIONS: Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status. CLINICAL RELEVANCE STATEMENT: The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment. KEY POINTS: • Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

Multi-omics integrative modelling for stereotactic body radiotherapy in early-stage non-small cell lung cancer: clinical trial protocol of the MONDRIAN study.

BACKGROUND: Currently, main treatment strategies for early-stage non-small cell lung cancer (ES-NSCLC) disease are surgery or stereotactic body radiation therapy (SBRT), with successful local control rates for both approaches. However, regional and distant failure remain critical in SBRT, and it is paramount to identify predictive factors of response to identify high-risk patients who may benefit from more aggressive approaches. The main endpoint of the MONDRIAN trial is to identify multi-omic biomarkers of SBRT response integrating information from the individual fields of radiomics, genomics and proteomics. METHODS: MONDRIAN is a prospective observational explorative cohort clinical study, with a data-driven, bottom-up approach. It is expected to enroll 100 ES-NSCLC SBRT candidates treated at an Italian tertiary cancer center with well-recognized expertise in SBRT and thoracic surgery. To identify predictors specific to SBRT, MONDRIAN will include data from 200 patients treated with surgery, in a 1:2 ratio, with comparable clinical characteristics. The project will have an overall expected duration of 60 months, and will be structured into five main tasks: (i) Clinical Study; (ii) Imaging/ Radiomic Study, (iii) Gene Expression Study, (iv) Proteomic Study, (v) Integrative Model Building. DISCUSSION: Thanks to its multi-disciplinary nature, MONDRIAN is expected to provide the opportunity to characterize ES-NSCLC from a multi-omic perspective, with a Radiation Oncology-oriented focus. Other than contributing to a mechanistic understanding of the disease, the study will assist the identification of high-risk patients in a largely unexplored clinical setting. Ultimately, this would orient further clinical research efforts on the combination of SBRT and systemic treatments, such as immunotherapy, with the perspective of improving oncological outcomes in this subset of patients. TRIAL REGISTRATION: The study was prospectively registered at clinicaltrials.gov (NCT05974475).

Comparison of automated segmentation techniques for magnetic resonance images of the prostate.

BACKGROUND: Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs. METHODS: This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables. RESULTS: The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables. CONCLUSIONS: Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855[Formula: see text]0.887 Dice).

The T-N tract involvement as a new prognostic factor for PORT in locally advanced oral cavity tumors.

OBJECTIVE: The space comprised between tumor and neck lymph nodes (T-N tract) is one of the main routes of tumor spread in oral cavity tumors. Aim of the study was to investigate the impact of T-N tract involvement on the postoperative radiotherapy (PORT) outcomes. MATERIALS AND METHODS: Patients (pts) treated between 2000 and 2016 with indication to PORT were retrospectively retrieved. Inclusion criteria were: (a) locally advanced tumors of the oral cavity, (b) who received with indication to PORT (c) with a minimum follow-up of six months. RESULTS: One hundred and fifty-seven pts met the inclusion criteria (136 pts treated with PORT and 21 pts not treated with PORT). In the PORT cohort, the T-N tract involvement had no impact on both OS (p = .09) and LRFS (p = .2). Among the non-PORT cohort, both OS (p = .007) and LRFS (p = .017) were worse for pts with positive T-N tract compared to those with negative T-N tract. PORT improved both OS (p = .008) and LRFS (p = .003) in pts with positive T-N tract but not in those with negative T-N tract (p = .36 and p = .37, respectively). CONCLUSIONS: Our results suggest that involvement of T-N tract should be considered as prognostic factors informing the indication to PORT.

A narrative review for radiation oncologists to implement preoperative partial breast irradiation.

The strategy to anticipate radiotherapy (RT) before surgery, for breast cancer (BC) treatment, has recently generated a renewed interest. Historically, preoperative RT has remained confined either to highly selected patients, in the context of personalized therapy, or to clinical research protocols. Nevertheless, in the recent years, thanks to technological advances and increased tumor biology understanding, RT has undergone great changes that have also impacted the preoperative settings, embracing the modern approach to breast cancer. In particular, the reappraisal of preoperative RT can be viewed within the broader view of personalized and tailored medicine. In fact, preoperative accelerated partial breast irradiation (APBI) allows a more precise target delineation, with less variability in contouring among radiation oncologists, and a smaller treatment volume, possibly leading to lower toxicity and to dose escalation programs. The aim of the present review, which represents a benchmark study for the AIRC IG-23118, is to report available data on different technical aspects of preoperative RT including dosimetric studies, patient's selection and set-up, constraints, target delineation and clinical results. These data, along with the ones that will become available from ongoing studies, may inform the design of the future trials and representing a step toward a tailored APBI approach with the potential to challenge the current treatment paradigm in early-stage BC.Trial registration: The study is registered at clinicaltrials.gov (NCT04679454).

A Randomized Trial of Robotic Mastectomy Versus Open Surgery in Women With Breast Cancer or BrCA Mutation.

OBJECTIVE: The aim of this study was to compare robotic mastectomy with open classical technique outcomes in breast cancer patients. SUMMARY BACKGROUND DATA: As the use of robotic nipple sparing mastectomy continues to rise, improved understanding of the surgical, oncologic, and quality of life outcomes is imperative for appropriate patient selection as well as to better understand indications, limits, advantages, and dangers. METHODS: In a phase III, open label, single-center, randomized controlled trial involving 80 women with breast cancer (69) or with BRCA mutation (11), we compared the outcome of robotic and open nipple sparing mastectomy. Primary outcomes were surgical complications and quality of life using specific validated questionnaires. Secondary objective included oncologic outcomes. RESULTS: Robotic procedure was 1 hour and 18 minutes longer than open (P < 0.001). No differences in the number or type of complications (P = 0.11) were observed. Breast-Q scores in satisfaction with breasts, psychosocial, physical and sexual well-being were significantly higher after robotic mastectomy versus open procedure. Respect to baseline, physical and sexual well-being domains remained stable after robotic mastectomy, whereas they significantly decreased after open procedure (P < 0.02). The overall Body Image Scale questionnaire score was 20.7 ±â€Š13.8 versus 9.9 ±â€Š5.1 in the robotic versus open groups respectively, P < 0.0001. At median follow-up 28.6months (range 3.7-43.3), no local events were observed. CONCLUSIONS: Complications were similar among groups upholding the robotic technique to be safe. Quality of life was maintained after robotic mastectomy while significantly decrease after open surgery. Early follow-up confirm no premature local failure.ClinicalTrials.gov NCT03440398.

Hypofractionated proton therapy in breast cancer: where are we? A critical review of the literature.

PURPOSE: To critically review available literature on hypofractionated (≥ 3 Gy/fraction) proton therapy (PT) for breast cancer (BCa). METHODS: A systematic screening of the literature was performed in April 2021 in compliance with the preferred reporting items for systematic reviews and meta-analyses recommendations. All full-text publication written in English were considered eligible. Acute and late toxicities, oncological outcomes and dosimetric features were considered for the analysis. RESULTS: Twelve publications met the inclusion criteria; all studies but one focused on accelerated partial breast irradiation (APBI). Eleven works considered post-operative patients, one referred to ABPI as a curative-intent modality. The dosimetric profile of PT compared favorably with both photon-based 3D conformal and intensity-modulated techniques, while a more extended follow-up is warranted to fully assess both the long-term toxicities and the non-inferiority of oncological outcomes. CONCLUSION: Our work shows that results on PT for BCa are currently only available for APBI applications, with dosimetric analyses demonstrating a clear advantage over both 3D conformal and intensity modulated X-rays techniques, especially when ≥ 2 treatment fields were used. However, further evidence is needed to define whether such theoretical benefit translates into clinical improvements, especially in the long-term.