Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies.

Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressive growth potential and have the capacity to spread across diverse cerebral locations simultaneously. Magnetic resonance imaging (MRI) scans of individuals afflicted with metastatic brain tumors unveil a wide spectrum of characteristics. These lesions vary in size and quantity, spanning from tiny nodules to substantial masses captured within MRI. Patients may present with a limited number of lesions or an extensive burden of hundreds of them. Moreover, longitudinal studies may depict surgical resection cavities, as well as areas of necrosis or edema. Thus, the manual analysis of such MRI scans is difficult, user-dependent and cost-inefficient, and - importantly - it lacks reproducibility. We address these challenges and propose a pipeline for detecting and analyzing brain metastases in longitudinal studies, which benefits from an ensemble of various deep learning architectures originally designed for different downstream tasks (detection and segmentation). The experiments, performed over 275 multi-modal MRI scans of 87 patients acquired in 53 sites, coupled with rigorously validated manual annotations, revealed that our pipeline, built upon open-source tools to ensure its reproducibility, offers high-quality detection, and allows for precisely tracking the disease progression. To objectively quantify the generalizability of models, we introduce a new data stratification approach that accommodates the heterogeneity of the dataset and is used to elaborate training-test splits in a data-robust manner, alongside a new set of quality metrics to objectively assess algorithms. Our system provides a fully automatic and quantitative approach that may support physicians in a laborious process of disease progression tracking and evaluation of treatment efficacy.

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients.

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.

Sonographic Images of Hepatic Portal Venous Gas in a Patient with Gastrointestinal Ischemia.

Hepatic portal venous gas (HPVG) detected by ultrasound (US) following liver transplantation or in suppurative cholangitis was described previously. To our knowledge, there have been no descriptions of HPVG detected by US in acute mesenteric ischemia. Here we present diagnostic images of a 52-year-old female who was admitted to the intensive care unit (ICU) following successful embolization of a ruptured saccular aneurysm of the right vertebral artery. During their stay in the ICU, the patient developed hypotension with low systemic vascular resistance and hypovolemia. Based on physical examination of the abdomen and laboratory results, preliminary diagnosis of intra-abdominal sepsis was made. Early abdominal US was performed to find the source of sepsis. The preliminary diagnosis of stomach/small intestine ischemia was made by ultrasonic detection of HPVG. Other less likely diagnoses were pneumobilia due to cholangitis, hepatic micro-abscesses, and punctuate calcifications. The diagnosis was confirmed by multi-phase abdominal computed tomography. The explorative laparotomy revealed necrosis of the stomach, small intestine, and liver. Due to the severity of necrosis, surgical treatment was abandoned. Provided sonographic images show HPVG as an ominous sign of small intestine and stomach ischemia. Early liver US should be performed whenever intra-abdominal pathology is suspected.

Segmenting pediatric optic pathway gliomas from MRI using deep learning.

Optic pathway gliomas are low-grade neoplastic lesions that account for approximately 3-5% of brain tumors in children. Assessing tumor burden from magnetic resonance imaging (MRI) plays a central role in its efficient management, yet it is a challenging and human-dependent task due to the difficult and error-prone process of manual segmentation of such lesions, as they can easily manifest different location and appearance characteristics. In this paper, we tackle this issue and propose a fully-automatic and reproducible deep learning algorithm built upon the recent advances in the field which is capable of detecting and segmenting optical pathway gliomas from MRI. The proposed training strategies help us elaborate well-generalizing deep models even in the case of limited ground-truth MRIs presenting example optic pathway gliomas. The rigorous experimental study, performed over two clinical datasets of 22 and 51 multi-modal MRIs acquired for 22 and 51 patients with optical pathway gliomas, and a public dataset of 494 pre-surgery low-/high-grade glioma patients (corresponding to 494 multi-modal MRIs), and involving quantitative, qualitative and statistical analysis revealed that the suggested technique can not only effectively delineate optic pathway gliomas, but can also be applied for detecting other brain tumors. The experiments indicate high agreement between automatically calculated and ground-truth volumetric measurements of the tumors and very fast operation of the proposed approach, both of which can increase the clinical utility of the suggested software tool. Finally, our deep architectures have been made open-sourced to ensure full reproducibility of the method over other MRI data.

Multifocality and Multicentrality in Breast Cancer: Comparison of the Efficiency of Mammography, Contrast-Enhanced Spectral Mammography, and Magnetic Resonance Imaging in a Group of Patients with Primarily Operable Breast Cancer.

BACKGROUND: The multifocality and multicentrality of breast cancer (MFMCC) are the significant aspects that determine a specialist's choice between applying breast-conserving therapy (BCT) or performing a mastectomy. This study aimed to assess the usefulness of mammography (MG), contrast-enhanced spectral mammography (CESM), and magnetic resonance imaging (MRI) in women diagnosed with breast cancer before qualifying for surgical intervention to visualize other (additional) cancer foci. METHODS: The study included 60 breast cancer cases out of 630 patients initially who underwent surgery due to breast cancer from January 2015 to April 2019. MG, CESM, and MRI were compared with each other in terms of the presence of MFMCC and assessed for compliance with the postoperative histopathological examination (HP). RESULTS: Histopathological examination confirmed the presence of MFMCC in 33/60 (55%) patients. The sensitivity of MG in detecting MFMCC was 50%, and its specificity was 95.83%. For CESM, the sensitivity was 85.29%, and the specificity was 96.15%. For MRI, all the above-mentioned parameters were higher as follows: sensitivity-91.18%; specificity-92.31%. CONCLUSIONS: In patients with MFMCC, both CESM and MRI are highly sensitive in the detection of additional cancer foci. Both CESM and MRI change the extent of surgical intervention in every fourth patient.