The involvement of axillary lymph node metastasis in breast cancer is one of the most important independent prognostic factors. While the metastasis of lymph node depends on primary tumour intrinsic behaviour, morphology and angioinvasivity, the involvement of the peritumoral tissue by the neoplastic cells also provides useful information for the potential tumour aggressiveness. The lymph node status is currently evaluated by histological invasive procedures with possible complications, asking for introducing safer approaches. Among different imaging techniques, the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) highlights physiological and morphological characteristics, reflecting breast lesions behaviour and aggressiveness. In the recent years, deep learning (DL) approaches, such as Convolutional Neural Networks, gained increasing popularity for biomedical image processing. Thanks to their ability to autonomously learn from images the set of features for the specific task to solve, they allow finding non-invasive alternatives to the standard procedures used up to now. This paper aims to evaluate the applicability of DL approaches for the axillary lymph node metastasis prediction, considering primary tumour DCE-MRI sequence. Differently from other work in the literature, we include a detailed analysis of healthy tissue influence in lymph node tumour spread through the evaluation of different tumour bounding options. Promising results are reported on a dataset of 153 patients with 155 malignant lesions.
Nowadays information diffusion has become more and more immediate and fast thanks to social media and its services. However, lack of controls and moderation in resources as social microblogs often leads to spread unverified information, such as rumours, which can become a threat to the society. To improve life quality and good information diffusion, various automatic systems have been studied for rumour detection in microblogs at level of aggregation of posts, whereas a few effort has been tried to the most challenging scenario where the rumour has to be recognized at level of each single post. In this work, we direct our efforts towards individual post rumour detection: we investigate how features describing influence potential, personal interest and network characteristic perform on two different datasets of posts collected from Twitter using two different health-related keywords. As a further contribution, we study what happens in cross-topic tests, i.e. when the rumour detection system is trained with posts with an hashtag and tested on samples with a different one.
The possibility of planning a therapy minimizing side effects and optimizing efficacy of cancer treatments is one of the main challenges tackled by precision medicine research in oncology. In this context, radiomics is revealing itself as a promising path for better understanding the correct approach to personalized cures. Its primary aim is to go beyond basic medical images analysis, which only leverages on direct measurements on the tumor mass, i.e. dimension and shape. On the contrary, radiomics approach is oriented to the extraction of heterogeneous and quantitative data from the images to characterize the disease from a wider perspective, in order to provide the physician a valid support for the therapy decision and survival prediction. This manuscript presents an application of radiomics to Non-Small Cell Lung Cancer, dealing with the novel task of predicting the possibility to carry out an adaptive therapy. We achieved promising performance, reporting a radiomics signature for predicting tumor reduction during therapy.
The year 2020 was marked by the worldwide COVID-19 pandemic, which caused over 2.5 million deaths by the end of February 2021. Different methods have been established since the beginning to identify infected patients and restrict the spread of the virus. In addition to laboratory analysis, used as the gold standard, several applications have been developed to apply deep learning algorithms to chest X-ray (CXR) images to diagnose patients affected by COVID-19. The literature shows that convolutional neural networks (CNNs) perform well on a single image dataset, but fail to generalize to other sources of data. To overcome this limitation, we present a late fusion approach in which multiple CNNs collaborate to diagnose the CXR scan of a patient, improving the generalizability. Experiments on three datasets publicly available show that the ensemble of CNNs outperforms stand-alone networks, achieving promising performance not only in cross-validation, but also when external validation is used, with an average accuracy of 95.18%.
Background: to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. Methods: 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. Results: edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). Conclusions: radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data.
The use of deep neural networks (DNNs) in medical images has enabled the development of solutions characterized by the need of leveraging information coming from multiple sources, raising the Multimodal Deep Learning. DNNs are known for their ability to provide hierarchical and high-level representations of input data. This capability has led to the introduction of methods performing data fusion at an intermediate level, preserving the distinctiveness of the heterogeneous sources in modality-specific paths, while learning the way to define an effective combination in a shared representation. However, modeling the intricate relationships between different data remains an open issue. In this paper, we aim to improve the integration of data coming from multiple sources. We introduce between layers belonging to different modality-specific paths a Transfer Module (TM) able to perform the cross-modality calibration of the extracted features, reducing the effects of the less discriminative ones. As case of study, we focus on the axillary lymph nodes metastasis evaluation in malignant breast cancer, a crucial prognostic factor, affecting patient's survival. We propose a Multi-Input Single-Output 3D Convolutional Neural Network (CNN) that considers both images acquired with multiparametric Magnetic Resonance and clinical information. In particular, we assess the proposed methodology using four architectures, namely BasicNet and three ResNet variants, showing the improvement of the performance obtained by including the TM in the network configuration. Our results achieve up to 90% and 87% of accuracy and Area under ROC curve, respectively when the ResNet10 is considered, surpassing various fusion strategies proposed in the literature.
Vestibular schwannomas, also known as acoustic neuromas, are benign primary intracranial tumor of the myelin-forming cells of the 8th cranial nerve. Stereotactic radiosurgery is one of the available therapies that can effectively control tumor growth, and it can be performed using the CyberKnife robotic device. However, this therapy may have side effects and its efficacy should be assessed up to two years. In this respect, being able to forecast the treatment response using the data collected during the initial and routinely MR images could be a valuable support when planning a personalised therapy. This manuscript therefore introduces a machine learning-based radiomics approach that first computes quantitative biomarkers from MR images and then predicts the treatment response, taking also into consideration the dataset class skewness.
The exponential growth of IoT devices, smartphones, smartwatches, and vehicles equipped with positioning technology, such as Global Positioning System (GPS) modules, has boosted the development of location-based services for several applications in Intelligent Transportation Systems. However, the inherent error of location-based technologies makes it necessary to align the positioning trajectories to the actual underlying road network, a process known as map-matching. To the best of our knowledge, there are no comprehensive tools that allow us to model street networks, conduct topological and spatial analyses of the underlying street graph, perform map-matching processes on GPS point trajectories, and deeply analyse and elaborate these reconstructed trajectories. To address this issue, we present PyTrack, an open-source map-matching-based Python toolbox designed for academics, researchers and practitioners that integrate the recorded GPS coordinates with data provided by the OpenStreetMap, an open-source geographic information system. This manuscript overviews the architecture of the library offering a detailed description of its capabilities and modules. Besides, we provide an introductory guide to getting started with PyTrack covering the most fundamental steps of our framework. For more information on PyTrack, users are encouraged to visit the official repository at https://github.com/cosbidev/PyTrack or the official documentation at https://pytrack-lib.readthedocs.io .
