Sinding-Larsen-Johansson syndrome with minimally displaced patellar sleeve avulsion fracture: a case report with multimodality approach and literature review.

Sinding-Larsen-Johansson syndrome is an osteochondrosis affecting the inferior pole of the patella. Most cases can be easily diagnosed with adequate clinical history, physical examination, and proper imaging, including conventional radiography, ultrasound, and magnetic resonance imaging. Differentiating this condition from patellar sleeve avulsion fractures is important, since treatment is frequently surgical in the latter. Overlap between these two conditions can also occur. We present a case of an 11-year-old boy, with Sinding-Larsen-Johansson syndrome on both knees and a minimally displaced acute patellar avulsion sleeve fracture of the left knee, which was treated conservatively.

Machine learning in predicting extracapsular extension (ECE) of prostate cancer with MRI: a protocol for a systematic literature review.

INTRODUCTION: In patients with prostate cancer (PCa), the detection of extracapsular extension (ECE) and seminal vesicle invasion is not only important for selecting the appropriate therapy but also for preoperative planning and patient prognosis. It is of paramount importance to stage PCa correctly before surgery, in order to achieve better surgical and outcome results. Over the last years, MRI has been incorporated in the classical prostate staging nomograms with clinical improvement accuracy in detecting ECE, but with variability between studies and radiologist's experience. METHODS AND ANALYSIS: The research question, based on patient, index test, comparator, outcome and study design criteria, was the following: what is the diagnostic performance of artificial intelligence algorithms for predicting ECE in PCa patients, when compared with that of histopathological results after radical prostatectomy. To answer this question, we will use databases (EMBASE, PUBMED, Web of Science and CENTRAL) to search for the different studies published in the literature and we use the QUADA tool to evaluate the quality of the research selection. ETHICS AND DISSEMINATION: This systematic review does not require ethical approval. The results will be disseminated through publication in a peer-review journal, as a chapter of a doctoral thesis and through presentations at national and international conferences. PROSPERO REGISTRATION NUMBER: CRD42020215671.

Herlyn-Werner-Wunderlich syndrome: A case report in a young woman, with literature review.

Herlyn-Werner-Wunderlich syndrome is a rare complex congenital disorder, with combined Müllerian and mesonephric duct anomalies, presenting with uterus didelphys, unilateral blind hemivagina and ipsilateral renal agenesis. Hemivaginal obstruction usually leads to impairment of normal menstrual flow, resulting in symptoms after menarche, namely dysmenorrhea, pelvic pain or infertility. Age of presentation depends on the anatomical features of this anomaly. We report a case of a 21-year-old female presenting with few symptoms and incidental findings on transvaginal ultrasound, with typical findings of this disorder on magnetic resonance imaging, which remains the gold standard imaging technique for thorough assessment of Herlyn-Werner-Wunderlich syndrome, allowing for a correct diagnosis and adequate surgical management. Our case also highlights some unusual features, such as the presence of a blind ectopic ureter, with hematic content, and an incomplete septum within the obstructed hemivagina.

Ovarian clear cell carcinoma arising in a large endometrioma - A case report with pathological correlation and literature review.

Endometriosis-associated ovarian cancer represents the most common form of malignancy associated with this benign disease. It has a better prognosis than most types of ovarian cancer, with endometrioid adenocarcinoma and clear cell carcinoma as the main histological types. Clinical presentation is usually nonspecific and tumor biomarkers can be misleading, since they can also be elevated in the presence of benign ovarian endometriosis. We report a case of a 52-year-old woman with known ovarian and deep pelvic endometriosis, who developed ovarian clear cell carcinoma within a large endometrioma. The imaging findings highlight the key role of magnetic resonance imaging in detecting suspicious features such as loss of the "T2 shading" sign, loss of high T1 signal of an endometrioma, or the presence of mural nodules. Early detection of these malignancies is fundamental for adequate surgical treatment and overall outcome.

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.

Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and "motivate" the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.

Multiple instance learning for lung pathophysiological findings detection using CT scans.

Lung diseases affect the lives of billions of people worldwide, and 4 million people, each year, die prematurely due to this condition. These pathologies are characterized by specific imagiological findings in CT scans. The traditional Computer-Aided Diagnosis (CAD) approaches have been showing promising results to help clinicians; however, CADs normally consider a small part of the medical image for analysis, excluding possible relevant information for clinical evaluation. Multiple Instance Learning (MIL) approach takes into consideration different small pieces that are relevant for the final classification and creates a comprehensive analysis of pathophysiological changes. This study uses MIL-based approaches to identify the presence of lung pathophysiological findings in CT scans for the characterization of lung disease development. This work was focus on the detection of the following: Fibrosis, Emphysema, Satellite Nodules in Primary Lesion Lobe, Nodules in Contralateral Lung and Ground Glass, being Fibrosis and Emphysema the ones with more outstanding results, reaching an Area Under the Curve (AUC) of 0.89 and 0.72, respectively. Additionally, the MIL-based approach was used for EGFR mutation status prediction - the most relevant oncogene on lung cancer, with an AUC of 0.69. The results showed that this comprehensive approach can be a useful tool for lung pathophysiological characterization.

Sharing Biomedical Data: Strengthening AI Development in Healthcare.

Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images.

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.

Automatic Lung Nodule Detection Combined With Gaze Information Improves Radiologists' Screening Performance.

Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mortality rates associated with the pathology. However, searching lung nodules is a high complexity task, which affects the success of screening programs. Whilst computer-aided detection systems can be used as second observers, they may bias radiologists and introduce significant time overheads. With this in mind, this study assesses the potential of using gaze information for integrating automatic detection systems in the clinical practice. For that purpose, 4 radiologists were asked to annotate 20 scans from a public dataset while being monitored by an eye tracker device, and an automatic lung nodule detection system was developed. Our results show that radiologists follow a similar search routine and tend to have lower fixation periods in regions where finding errors occur. The overall detection sensitivity of the specialists was 0.67±0.07, whereas the system achieved 0.69. Combining the annotations of one radiologist with the automatic system significantly improves the detection performance to similar levels of two annotators. Filtering automatic detection candidates only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false-positives.