Deep Learning Networks for Automatic Retroperitoneal Sarcoma Segmentation in Computerized Tomography

Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives

This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.

A semiautomatic segmentation method framework for pelvic bone tumors based on CT-MR multimodal images

The pelvic structure is complex and the tumor is poorly defined from the surrounding tissues. Finding the exact tumor resection margin based on the surgeon's clinical experience alone is a time-consuming and difficult task, which is a major factor leading to surgical failure. An accurate method for segmenting pelvic bone tumors is needed. In this paper, a semiautomatic segmentation method for pelvic bone tumors based on CT-MR multimodal images is presented. The method combines multiple medical prior knowledge and image segmentation algorithms. Finally, the segmentation results are visualized in three dimensions. We tested the proposed method on a collection of 10 cases (97 tumor MR images in total). The segmentation results were compared with the manual annotation of the physicians. On average, our method has an accuracy of 0.9358, a recall of 0.9278, an IOU value of 0.8697, a Dice value of 0.9280, and an AUC value of 0.9632. The average error of the 3D model was within the allowable range of the surgery. The proposed algorithm can accurately segment bone tumors in pelvic MR images regardless of tumor location, size, and other factors. It provides the possibility to assist pelvic bone tumor preservation surgery.

CT and MR Imaging of Retroperitoneal Sarcomas: A Practical Guide for the Radiologist

Primary retroperitoneal sarcomas (RPS) represent around 10-16% of all sarcomas, with liposarcomas and leiomyosarcomas being the most common subtypes. RPS have some peculiar characteristics, imaging appearances, worse prognosis, and complications compared to other locations of sarcoma. Commonly, RPS primarily present as large masses, progressively encasing adjacent structures, causing mass effect, and complications. RPS diagnosis is often challenging, and these tumors may be overlooked; however, failure to recognize RPS characteristics leads to a worse prognosis for the patients. Surgery is the only recognized curative treatment, but the anatomical constraints of the retroperitoneum limit the ability to achieve wide resection margins; therefore, these tumors have a high rate of recurrence, and require long-term follow-up. The radiologist has an important role in the diagnosis of RPS, the definition of their extent, and their follow-up. Specific knowledge of the main imaging findings is required to reach an early diagnosis, and, ultimately, to guarantee the best patient management. This article provides an overview of the current knowledge regarding cross-sectional imaging features of patients with retroperitoneal sarcomas, presenting tips and tricks to improve imaging diagnosis of RPS.

Image Processing and Analysis for Preclinical and Clinical Applications

Preclinical and clinical imaging aims to characterize and measure biological processes and diseases in animals [...]