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Background: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance. We suggested a proof of concept to define an ensemble approach useful for integrating different algorithms proposed to solve the same clinical task. Methods: The proposed approach was developed starting from a public database consisting of radiomic features extracted from CT images relating to 535 patients suffering from lung cancer. Seven algorithms were trained independently by participants in the AI4MP working group on Artificial Intelligence of the Italian Association of Physics in Medicine to discriminate metastatic from non-metastatic patients. The classification scores generated by these algorithms are used to train SVM classifier. The Explainable Artificial Intelligence approach is applied to the final model. The ensemble model was validated following an 80–20 hold-out and leave-one-out scheme on the training set. Results: Compared to individual algorithms, a more accurate result was achieved. On the independent test the ensemble model achieved an accuracy of 0.78, a F1-score of 0.57 and a log-loss of 0.49. Shapley values representing the contribution of each algorithm to the final classification result of the ensemble model were calculated. This information represents an added value for the end user useful for evaluating the appropriateness of the classification result on a particular case. It also allows us to evaluate on a global level which methodological approaches of the individual algorithms are likely to have the most impact. Conclusion: Our proposal represents an innovative approach useful for integrating different algorithms that populate the literature and which lays the foundations for future evaluations in broader application scenarios. [ABSTRACT FROM AUTHOR]

This topical review focuses on Patient-Specific Quality Assurance (PSQA) approaches to stereotactic body radiation therapy (SBRT). SBRT requires stricter accuracy than standard radiation therapy due to the high dose per fraction and the limited number of fractions. The review considered various PSQA methods reported in 36 articles between 01/2010 and 07/2022 for SBRT treatment. In particular comparison among devices and devices designed for SBRT, sensitivity and resolution, verification methodology, gamma analysis were specifically considered. The review identified a list of essential data needed to reproduce the results in other clinics, highlighted the partial miss of data reported in scientific papers, and formulated recommendations for successful implementation of a PSQA protocol. [ABSTRACT FROM AUTHOR]

A simple experiment, suitable for performing in an undergraduate physics laboratory, illustrates electromagnetic induction through the water entering into a cylindrical rubber tube by detecting the voltage developed across the tube in the direction transverse both to the flow velocity and to the magnetic field. The apparatus is a very simple example of an electromagnetic flowmeter, a device which is commonly used both in industrial and physiological techniques. The phenomenology observed is similar to that of the Hall effect in the absence of an electric current in the direction of motion of the carriers. The experimental results show a dependence on the intensity of the magnetic field and on the carrier velocity, in good agreement with the theory. Discussion of the system, based on classical electromagnetism, indicates that the effect depends only on the flow rate, and is independent both of the velocity profile and of the electrical conductivity of the medium. (Contains 6 figures and 4 footnotes.)

[Display omitted] • AI4MP-Challenge is the first AIFM multicentric experience on machine learning. • The main objective is to improve knowledge and skills of medical physicists on machine learning. • Encountered pitfalls: violation of independence assumption, computation errors, data imbalance. • Providing both cross-validation and an independent test helps to detect implementation issue. • The exclusion of non-robust features does not allow to significantly increase model stability. A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community. A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model). Both models were validated with cross-validation and on unseen data. The population stability index (PSI) was used as diagnostic metric for implementation issues. Performance was compared to reference. Base and robust models were compared in terms of performance and stability (coefficient of variation (CoV) of prediction probabilities). PSI detected potential implementation errors in 70 % of models. The dataset exhibited strong imbalance. The average Gmean (i.e. an appropriate metric for imbalance) among all participants was 0.67 ± 0.01, significantly higher than reference Gmean of 0.50 ± 0.04. Robust models performances were slightly worse than base models (p < 0.05). Regarding stability, robust models exhibited lower median CoV on training set only. AI4MP-Challenge models overperformed the reference, significantly improving the Gmean. Exclusion of less-robust features did not improve model robustness and it should be avoided when confounding effects are absent. Other methods, like harmonization or data augmentation, should be evaluated. This study demonstrated how the collaborative effort to foster knowledge on machine learning among medical physicists, through interactive sessions and exchange of information among participants, can result in improved models. [ABSTRACT FROM AUTHOR]

The intermethod agreement between automated algorithms for brainstem segmentation is investigated, focusing on the potential involvement of this structure in Autism Spectrum Disorders (ASD). Inconsistencies highlighted in previous studies on brainstem in the population with ASD may in part be a result of poor agreement in the extraction of structural features between different methods. A sample of 76 children with ASD and 76 age‐, gender‐, and intelligence‐matched controls was considered. Volumetric analyses were performed using common tools for brain structures segmentation, namely FSL‐FIRST, FreeSurfer (FS), and Advanced Normalization Tools (ANTs). For shape analysis SPHARM‐MAT was employed. Intermethod agreement was quantified in terms of Pearson correlations between pairs of volumes obtained by the different methods. The degree of overlap between segmented masks was quantified in terms of the Dice index. Both Pearson correlations and Dice indices, showed poor agreement between FSL‐FIRST and the other methods (ANTs and FS), which by contrast, yielded Pearson correlations greater than 0.93 and average Dice indices greater than 0.76 when compared with each other. As with volume, shape analyses exhibited discrepancies between segmentation methods, with particular differences noted between FSL‐FIRST and the others (ANT and FS), with under‐ and over‐segmentation in specific brainstem regions. These data suggest that research on brain structure alterations should cross‐validate findings across multiple methods. We consistently detected an enlargement of brainstem volume in the whole sample and in the male cohort across multiple segmentation methods, a feature particularly driven by the subgroup of children with idiopathic intellectual disability associated with ASD. [ABSTRACT FROM AUTHOR]

Abstract: A growing body of literature has identified volume alterations of the corpus callosum (CC) in subjects with autism spectrum disorders (ASD). However, to date very few investigations have been conducted on pre‐school‐age ASD children. This study aims to compare the volume of CC and its sub‐regions between pre‐schoolers with ASD and controls (CON) and to examine their relationship to demographic and clinical variables (sex, age, non‐verbal IQ ‐NVIQ‐, expressive non‐echolalic language, emotional and behavioural problems, and autism severity). The volume of CC of 40 pre‐schoolers with ASD (20 males and 20 females; mean age: 49 ± 12 months; mean NVIQ: 73 ± 22) and 40 sex‐, age‐, and NVIQ‐matched CON subjects (20 M and 20 F; mean age: 49 ± 14 months; mean NVIQ: 73 ± 23) were quantified applying the FreeSurfer automated parcellation software on Magnetic Resonance images. No significant volumetric differences in CC total volume and in its sub‐regions between ASD and CON were found using total brain volume as a covariate. Analogously, absence of CC volumetric differences was evident when boys and girls with ASD were compared with their matched controls. The CC total volume of younger ASD male subjects was found significantly larger with respect to matched CON, which is consistent with the atypical growth trajectory widely reported in these young children. The CC total volume was negatively correlated with autism severity, whereas no association between CC volume and other clinical variables was detected. If replicated, the indirect relationship between CC volume and autism severity suggests the involvement of CC in core ASD symptoms. [ABSTRACT FROM AUTHOR]

Background: To investigate changes of objective instrumental measures and correlate with patient reported outcomes (PROs) of radiation-induced dysphagia (RID) after swallowing organs at risk (SWOARs)-sparing IMRT., Methods: Patients (pts) underwent Fiberoptic Endoscopic Evaluation of Swallowing (FEES), Videofluoroscopy (VFS) and M.D. Anderson Dysphagia Inventory (MDADI) questionnaire at baseline, 6 and 12 months after treatment. They were categorized in two groups: MDADI-C ≥ 80 and MDADI-C < 80. Pharyngeal residue (PR) and penetration (P) or aspiration (A) were considered as surrogate of RID., Results: Between 2016 and 2022 we enrolled 75 pts, 40 (53 %) MDADI-C ≥ 80 and 35 (47 %) MDADI-C < 80 at baseline. Among MDADI-C ≥ 80 the mean baseline PR score at FEES was 0,42 rising to 1,36 at 6 months (p = 0,001) and stabilizing to 1,15 at 12 months (p = 0,21); indeed, the mean baseline PR score at VFS was 0,55 rising to 1 at 6 months (p = 0,069) and slightly dropping to 0,7 at 12 months (p = 0,069). Among MDADI-C < 80 the mean baseline PR score at FEES was 0,56 rising to 1,07 at 6 months (p = 0,012) and stabilizing to 1,07 at 12 months (p = 0,99); indeed the mean baseline PR score at VFS was 0,67 rising to 1,19 at 6 months (p = 0,04) and dropping to 0,78 at 12 months (p = 0,04). No correlation was found between PROs and objective measures., Conclusion: Our results show optimal acceptable deglutition preservation from major complications after SWOARs-sparing IMRT by means of low objective scores in both MDADI-C groups. Lack of correlation between PROs and objective measures suggest that referred RID is likely associated to persistence of SWOARs inflammation rather than to a real impairment of function., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)