Moddicom: a Complete and Easily Accessible Library for Prognostic Evaluations Relying on Image Features

Decision Support Systems (DSSs) are increasingly exploited in the area of prognostic evaluations. For predicting the effect of therapies on patients, the trend is now to use image features, i.e. information that can be automatically computed by considering images resulting by analysis. The DSSs application as predictive tools is particularly suitable for cancer treatment, given the peculiarities of the disease –which is highly localised and lead to significant social costs– and the large number of images that are available for each patient. At the state of the art, there exists tools that allow to handle image features for prognostic evaluations, but they are not designed for medical experts. They require either a strong engineering or computer science background since they do not integrate all the required functions, such as image retrieval and storage. In this paper we fill this gap by proposing Moddicom, a user-friendly complete library specifically designed to be exploited by physicians. A preliminary experimental analysis, performed by a medical expert that used the tool, demonstrates the efficiency and the effectiveness of Moddicom

Recommendations on how to establish evidence from auto-segmentation software in radiotherapy

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MR-guided radiotherapy for liver malignancies

MR guided radiotherapy represents one of the most promising recent technological innovations in the field. The possibility to better visualize therapy volumes, coupled with the innovative online adaptive radiotherapy and motion management approaches, paves the way to more efficient treatment delivery and may be translated in better clinical outcomes both in terms of response and reduced toxicity. The aim of this review is to present the existing evidence about MRgRT applications for liver malignancies, discussing the potential clinical advantages and the current pitfalls of this new technology

Are you planning to be a radiation oncologist? A survey by the young group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

Background and purpose The Young Section of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO) circulated an online questionnaire survey among residents currently enrolled within Italian radiotherapy residency schools to investigate the profiles, motivations, knowledge of the radiotherapy discipline, organizations and the needs of younger members.Materials and Methods The survey was developed by the yAIRO steering committee and included questions about the demo-graphic characteristics of the residents (Profile A), the background of their clinical experience during the school of medicine and national residency admission test performance (Profile B) and the residents' knowledge of the scientific associations active in the field of radiotherapy (Profile C).Results Out of 400 residents actually in training, 134 responded to the questionnaire (response rate 33.5%). According to most of the residents, radiotherapy was not adequately studied during the medical school (n. 95; 71%) and an Internship in Radiotherapy was not mandatory (n. 99; 74%). Only a minority of the residents had chosen to complete a master's degree thesis in radiotherapy (n. 12; 9%). A low percentage of the residents stated that they were aware of the Italian Association of Radiotherapy and Clinical Oncology (AIRO), its young section (yAIRO) and the European Society for Radiotherapy and Oncology (ESTRO) when they were in School of Medicine (respectively, 11%, 7% and 13%).Conclusions The results of the survey require a profound reflection on the current teaching methods of Radiation Oncology in our country, highlighting the need for a better integration in the framework of the School of Medicine core curriculum

Radiation therapy during the coronavirus disease 2019 (covid-19) pandemic in Italy: a view of the nation's young oncologists

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Tiles: an online algorithm for community discovery in dynamic social networks

Community discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify

Proposal for a New Diagnostic Histopathological Approach in the Evaluation of Ki-67 in GEP-NETs

Abstract Introduction: Studies have shown that the Ki-67 index is a valuable biomarker for the diagnosis, and classification of gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs). We re-evaluated the expression of Ki-67 based on the intensity of the stain, basing our hypothesis on the fact that the Ki-67 protein is continuously degraded. Background: The aim was to evaluate whether a new scoring method would be more effective in classifying NETs by reducing staining heterogeneity. Methods: Patients with GEP-NET (n = 87) were analyzed. The classification difference between the two methods was determined. Results: The classification changed significantly when the Ki-67 semiquantal index was used. The percentage of G1 patients increased from 18.4% to 60.9%, while the G2 patients decreased from 66.7% to 29.9% and the G3 patients also decreased from 14.9% to 9.2%. Moreover, it was found that the traditional Ki-67 was not significantly related to the overall survival (OS), whereas the semiquantal Ki-67 was significantly related to the OS. Conclusions: The new quantification was a better predictor of OS and of tumor classification. Therefore, it could be used both as a marker of proliferation and as a tool to map tumor dynamics that can influence the diagnosis and guide the choice of therapy

Early immunopathological diagnosis of ichthyosis with confetti in two sporadic cases with new mutations in keratin 10.

Ichthyosis with confetti (IC) is a severe non-syndromic ichthyosis due to heterozygous mutations in the KRT10 gene. The disease manifests at birth with erythroderma and scaling and is characterised by the gradual development of numerous confetti-like spots of normal skin. Diagnosis of IC is frequently delayed until adolescence or even adulthood. We report 2 young children who were first diagnosed as having congenital ichthyosiform erythroderma. However, the development of thick, confluent hyperkeratotic plaques together with the histopathological finding of keratinocyte vacuolisation in the suprabasal epidermis evoked IC. Immunofluorescence analysis showed a highly reduced keratin 10 expression within the cytoplasm of suprabasal keratinocytes and its characteristic mislocalisation to the nuclei. The diagnosis was confirmed by the identification of 2 previously unreported mutations in intron 6 and exon 7 of KRT10. Careful clinical examination then showed the presence of the first spots of normal skin in both patients at the age of 2.5 and 5 years, respectively. These cases point to the usefulness of immunofluorescence analysis of keratin 10 expression for an early diagnosis of IC

Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy

Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance