Ten daily fractions for partial breast irradiation. Long-term results of a prospective phase II trial.

Partial breast irradiation (PBI) is an effective adjuvant treatment after breast conservative surgery for selected early-stage breast cancer patients. However, the best fractionation scheme is not well defined. Hereby, we report the 5-year clinical outcome and toxicity of a phase II prospective study of a novel regimen to deliver PBI, which consists in 40 Gy delivered in 10 daily fractions. Patients with early-stage (pT1-pT2, pN0-pN1a, M0) invasive breast cancer were enrolled after conservative surgery. The minimum age at diagnosis was 60 years old. PBI was delivered with 3D-conformal radiotherapy technique with a total dose of 40 Gy, fractionated in 10 daily fractions (4 Gy/fraction). Eighty patients were enrolled. The median follow-up was 67 months. Five-year local control (LC), disease-free survival (DFS), and overall survival (OS) were 95%, 91%, and 96%, respectively. Grade I and II subcutaneous fibrosis were documented in 23% and 5% of cases. No grade III late toxicity was observed. PBI delivered in 40 Gy in 10 daily fractions provided good clinical results and was a valid radiotherapy option for early-stage breast cancer patients

Partial prostate re-irradiation for the treatment of isolated local recurrence of prostate cancer in patients previously treated with primary external beam radiotherapy: short-term results of a monocentric study

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In Doxorubicin-Adapted Hodgkin Lymphoma Cells, Acquiring Multidrug Resistance and Improved Immunosuppressive Abilities, Doxorubicin Activity Was Enhanced by Chloroquine and GW4869

Classical Hodgkin lymphoma (cHL) is a highly curable disease (70–80%), even though long-term toxicities, drug resistance, and predicting clinical responses to therapy are major challenges in cHL treatment. To solve these problems, we characterized two cHL cell lines with acquired resistance to doxorubicin, KM-H2dx and HDLM-2dx (HRSdx), generated from KM-H2 and HDLM-2 cells, respectively. HRSdx cells developed cross-resistance to vinblastine, bendamustin, cisplatin, dacarbazine, gemcitabine, brentuximab vedotin (BV), and γ-radiation. Both HDLM-2 and HDLM-2dx cells had intrinsic resistance to BV but not to the drug MMAE. HDLM-2dx acquired cross-resistance to caelyx. HRSdx cells had in common decreased CD71, CD80, CD54, cyt-ROS, HLA-DR, DDR1, and CD44; increased Bcl-2, CD58, COX2, CD26, CCR5, and invasive capability; increased CCL5, TARC, PGE2, and TGF-β; and the capability of hijacking monocytes. In HRSdx cells less sensitive to DNA damage and oxidative stress, the efflux drug transporters MDR1 and MRP1 were not up-regulated, and doxorubicin accumulated in the cytoplasm rather than in the nucleus. Both the autophagy inhibitor chloroquine and extracellular vesicle (EV) release inhibitor GW4869 enhanced doxorubicin activity and counteracted doxorubicin resistance. In conclusion, this study identifies common modulated antigens in HRSdx cells, the associated cross-resistance patterns, and new potential therapeutic options to enhance doxorubicin activity and overcome resistance

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare

Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts

Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's image and perform a binary classification of the occurrence of a given clinical endpoint. In this work, a 2D-CNN and a 3D-CNN for the binary classification of distant metastasis (DM) occurrence in head and neck cancer patients were extended to perform time-to-event analysis. The newly built CNNs incorporate censoring information and output DM-free probability curves as a function of time for every patient. In total, 1037 patients were used to build and assess the performance of the time-to-event model. Training and validation was based on 294 patients also used in a previous benchmark classification study while for testing 743 patients from three independent cohorts were used. The best network could reproduce the good results from 3-fold cross validation Harrell's concordance indices (HCIs) of 0.78, 0.74 and 0.80 in two out of three testing cohorts (HCIs of 0.88, 0.67 and 0.77). Additionally, the capability of the models for patient stratification into high and low-risk groups was investigated, the CNNs being able to significantly stratify all three testing cohorts. Results suggest that image-based deep learning models show good reliability for DM time-to-event analysis and could be used for treatment personalisation

Machine and deep learning methods for radiomics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155519/1/mp13678_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155519/2/mp13678.pd

Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure

none13noArtificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients’ care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.openRetico, Alessandra; Avanzo, Michele; Boccali, Tommaso; Bonacorsi, Daniele; Botta, Francesca; Cuttone, Giacomo; Martelli, Barbara; Salomoni, Davide; Spiga, Daniele; Trianni, Annalisa; Stasi, Michele; Iori, Mauro; Talamonti, CinziaRetico, Alessandra; Avanzo, Michele; Boccali, Tommaso; Bonacorsi, Daniele; Botta, Francesca; Cuttone, Giacomo; Martelli, Barbara; Salomoni, Davide; Spiga, Daniele; Trianni, Annalisa; Stasi, Michele; Iori, Mauro; Talamonti, Cinzi

Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model

The aim of this study is to predict local failure after partial prostate re-irradiation for the treatment of isolated locally recurrent prostate cancer by using a machine learning classifier based on radiomic features from pre-treatment computed tomography (CT), positron-emission tomography (PET) and biological effective dose distribution (BED) of the radiotherapy plan. The analysis was conducted on a monocentric dataset of 43 patients with evidence of isolated intraprostatic recurrence of prostate cancer after primary external beam radiotherapy. All patients received partial prostate re-irradiation delivered by volumetric modulated arc therapy. The gross tumor volume (GTV) of each patient was manually contoured from planning CT, choline-PET and dose maps. An ensemble machine learning pipeline including unbalanced data correction and feature selection was trained using the radiomic and dosiomic features as input for predicting occurrence of local failure. The model performance was assessed using sensitivity, specificity, accuracy and area under receiver operating characteristic curves of the score function in 10-fold cross validation repeated 100 times. Local failure was observed in 13 patients (30%), with a median time to recurrence of 36.7 months (range = 6.1–102.4 months). A four variables ensemble machine learning model resulted in accuracy of 0.62 and AUC 0.65. According to our results, a dosiomic machine learning classifier can predict local failure after partial prostate re-irradiation

A Novel Benchmarking Approach to Assess the Agreement among Radiomic Tools

Background: The translation of radiomic models into clinical practice is hindered by the limited reproducibility of features across software and studies. Standardization is needed to accelerate this process and to bring radiomics closer to clinical deployment.Purpose: To assess the standardization level of seven radiomic software programs and investigate software agreement as a function of built-in image preprocessing (eg, interpolation and discretization), feature aggregation methods, and the morphological characteristics (ie, volume and shape) of the region of interest (ROI).Materials and Methods: The study was organized into two phases: In phase I, the two Image Biomarker Standardization Initiative (IBSI) phantoms were used to evaluate the IBSI compliance of seven software programs. In phase II, the reproducibility of all IBSI-standardized radiomic features across tools was assessed with two custom Italian multicenter Shared Understanding of Radiomic Extractors (ImSURE) digital phantoms that allowed, in conjunction with a systematic feature extraction, observations on whether and how feature matches between program pairs varied depending on the preprocessing steps, aggregation methods, and ROI characteristics.Results: In phase I, the software programs showed different levels of completeness (ie, the number of computable IBSI benchmark values). However, the IBSI-compliance assessment revealed that they were all standardized in terms of feature implementation. When considering additional preprocessing steps, for each individual program, match percentages fell by up to 30%. In phase II, the ImSURE phantoms showed that software agreement was dependent on discretization and aggregation as well as on ROI shape and volume factors.Conclusion: The agreement of radiomic software varied in relation to factors that had already been standardized (eg, interpolation and discretization methods) and factors that need standardization. Both dependences must be resolved to ensure the reproducibility of radiomic features and to pave the way toward the clinical adoption of radiomic models. Published under a CC BY 4.0 license