Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer

Abstract: Background: Ovarian cancer survival rates have not changed in the last 20 years. The majority of cases are High-grade serous ovarian carcinomas (HGSOCs), which are typically diagnosed at an advanced stage with multiple metastatic lesions. Taking biopsies of all sites of disease is infeasible, which challenges the implementation of stratification tools based on molecular profiling. Main body: In this review, we describe how these challenges might be overcome by integrating quantitative features extracted from medical imaging with the analysis of paired genomic profiles, a combined approach called radiogenomics, to generate virtual biopsies. Radiomic studies have been used to model different imaging phenotypes, and some radiomic signatures have been associated with paired molecular profiles to monitor spatiotemporal changes in the heterogeneity of tumours. We describe different strategies to integrate radiogenomic information in a global and local manner, the latter by targeted sampling of tumour habitats, defined as regions with distinct radiomic phenotypes. Conclusion: Linking radiomics and biological correlates in a targeted manner could potentially improve the clinical management of ovarian cancer. Radiogenomic signatures could be used to monitor tumours during the course of therapy, offering additional information for clinical decision making. In summary, radiogenomics may pave the way to virtual biopsies and treatment monitoring tools for integrative tumour analysis

Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer

Funder: Horizon 2020; doi: http://dx.doi.org/10.13039/501100007601Funder: Cancer Research UKFunder: Mark Foundation For Cancer Research; doi: http://dx.doi.org/10.13039/100014599Abstract: Purpose: To develop a precision tissue sampling technique that uses computed tomography (CT)–based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies that can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC). Methods: Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy. Results: We successfully co-registered CT-based radiomic tumour habitats with US images in all patients. The median time between CT scan and biopsy was 21 days (range 7–30 days). The median DSC for tumour-specific CT/US fusion accuracy was 0.53 (range 0.79 to 0.37). The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76–0.79) while it was lower for the smaller omental metastases (DSC: 0.37–0.53). Conclusion: We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC. Key Points: • We developed a prevision tissue sampling technique that co-registers CT-based radiomics–based tumour habitats with US images. • The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76–0.79) while it was lower for the smaller omental metastases (DSC: 0.37–0.53)

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts

Unravelling the Spatial and Temporal Heterogeneity of High-Grade Serous Ovarian Cancer Using Imaging-Based Biomarkers

High-Grade Serous Ovarian Cancer (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, characterised by significant spatial and temporal heterogeneity, which has been linked to poor survival outcomes. In this challenging landscape, the quest to characterise heterogeneity through non-invasive image-based biomarkers extracted from routinely collected radiological data is paramount. These biomarkers hold the potential to improve predictions of patient diagnosis, prognosis and treatment response. Radiomics, an emerging computational approach, offers a promising non-invasive method to assess tumour heterogeneity using radiological images. The biological validation of radiomic features (e.g. through genomics, proteomics or transcriptomics) aims to deepen our understanding of tumour and tumour microenvironment biology and translate radiomics into clinically relevant insights. This thesis addresses technical barriers in integrating radiogenomics into HGSOC clinical settings, as well as leveraging radiomics for treatment response prediction. It includes four technical contributions, with a comprehensive background chapter introducing concepts and challenges in HGSOC, Computed Tomography (CT), ultrasonography (US), radiomics and radiogenomics to unfamiliar readers. Two chapters focus on multi-modal co-registration algorithms for radiogenomics studies. Chapter 3 refines multi-modal image registration for real-time US-guided biopsies of CT-derived habitats, enhancing the degrees of freedom of a commercially available software to accommodate complex abdominal deformations. Additionally, a novel real-time quantification metric assesses registration fidelity. In Chapter 4, a clinical pipeline for generating 3D-printed moulds for image-tissue registration of resected pelvic and ovarian tumours is described, developed through a pilot trial in a clinical setting, detailing the iterative process. Furthermore, radiomics may correlate with clinical endpoints, providing biomarkers for diagnosis, prognosis, and treatment response evaluation. In Chapter 5, novel image-based biomarkers and radiomic features are integrated into a sequential treatment clinical trial to predict and compare PARP inhibitor and immunotherapy responses over time. Chapter 6 presents an interpretable radiomics framework as a feasibility study to predict changes in tumour composition under neoadjuvant chemotherapy (NACT) from pre-treatment baseline scans. Throughout the investigations, practical constraints and potential improvements have been identified and discussed.W.D. Armstrong Trust Fun

A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection

Malaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial treatment, reducing the mortality rate, and points to other illnesses in negative cases. However, the gold standard remains the light microscopy of May-Grünwald–Giemsa (MGG)-stained thin and thick peripheral blood (PB) films. This is a time-consuming procedure, dependent on a pathologist’s skills, meaning that healthcare providers may encounter difficulty in diagnosing malaria in places where it is not endemic. This work presents a novel three-stage pipeline to (1) segment erythrocytes, (2) crop and mask them, and (3) classify them into malaria infected or not. The first and third steps involved the design, training, validation and testing of a Segmentation Neural Network and a Convolutional Neural Network from scratch using a Graphic Processing Unit. Segmentation achieved a global accuracy of 93.72% over the test set and the specificity for malaria detection in red blood cells (RBCs) was 87.04%. This work shows the potential that deep learning has in the digital pathology field and opens the way for future improvements, as well as for broadening the use of the created networks

A deep learning approach for segmentation of red blood cell images and malaria detection

Malaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial treatment, reducing the mortality rate, and points to other illnesses in negative cases. However, the gold standard remains the light microscopy of May-Grünwald–Giemsa (MGG)-stained thin and thick peripheral blood (PB) films. This is a time-consuming procedure, dependent on a pathologist’s skills, meaning that healthcare providers may encounter difficulty in diagnosing malaria in places where it is not endemic. This work presents a novel three-stage pipeline to (1) segment erythrocytes, (2) crop and mask them, and (3) classify them into malaria infected or not. The first and third steps involved the design, training, validation and testing of a Segmentation Neural Network and a Convolutional Neural Network from scratch using a Graphic Processing Unit. Segmentation achieved a global accuracy of 93.72% over the test set and the specificity for malaria detection in red blood cells (RBCs) was 87.04%. This work shows the potential that deep learning has in the digital pathology field and opens the way for future improvements, as well as for broadening the use of the created networks.Peer ReviewedPostprint (published version

Cerebral Oxygenation Responses to Standing in Young Patients with Vasovagal Syncope.

Peer reviewed: TrueFunder: Fundació Universitària Agustí Pedro i Pons, Universitat de BarcelonaVasovagal syncope (VVS) is common in young adults and is attributed to cerebral hypoperfusion. However, during active stand (AS) testing, only peripheral and not cerebral hemodynamic responses are measured. We sought to determine whether cerebral oxygenation responses to an AS test were altered in young VVS patients when compared to the young healthy controls. A sample of young healthy adults and consecutive VVS patients attending a Falls and Syncope unit was recruited. Continuous beat-to-beat blood pressure (BP), heart rate, near-infrared spectroscopy (NIRS)-derived tissue saturation index (TSI), and changes in concentration of oxygenated/deoxygenated Δ[O2Hb]/Δ[HHb] hemoglobin were measured. BP and NIRS-derived features included nadir, peak, overshoot, trough, recovery rate, normalized recovery rate, and steady-state. Multivariate linear regression was used to adjust for confounders and BP. In total, 13 controls and 27 VVS patients were recruited. While no significant differences were observed in the TSI and Δ[O2Hb], there was a significantly smaller Δ[HHb] peak-to-trough and faster Δ[HHb] recovery rate in VVS patients, independent of BP. A higher BP steady-state was observed in patients but did not remain significant after multiple comparison correction. Young VVS patients demonstrated a similar cerebral circulatory response with signs of altered peripheral circulation with respect to the controls, potentially due to a hyper-reactive autonomic nervous system. This study sets the grounds for future investigations to understand the role of cerebral regulation during standing in VVS

Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer.

BACKGROUND: Ovarian cancer survival rates have not changed in the last 20 years. The majority of cases are High-grade serous ovarian carcinomas (HGSOCs), which are typically diagnosed at an advanced stage with multiple metastatic lesions. Taking biopsies of all sites of disease is infeasible, which challenges the implementation of stratification tools based on molecular profiling. MAIN BODY: In this review, we describe how these challenges might be overcome by integrating quantitative features extracted from medical imaging with the analysis of paired genomic profiles, a combined approach called radiogenomics, to generate virtual biopsies. Radiomic studies have been used to model different imaging phenotypes, and some radiomic signatures have been associated with paired molecular profiles to monitor spatiotemporal changes in the heterogeneity of tumours. We describe different strategies to integrate radiogenomic information in a global and local manner, the latter by targeted sampling of tumour habitats, defined as regions with distinct radiomic phenotypes. CONCLUSION: Linking radiomics and biological correlates in a targeted manner could potentially improve the clinical management of ovarian cancer. Radiogenomic signatures could be used to monitor tumours during the course of therapy, offering additional information for clinical decision making. In summary, radiogenomics may pave the way to virtual biopsies and treatment monitoring tools for integrative tumour analysis

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study.

Peer reviewed: TrueBACKGROUND: High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours. METHODS: In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process. RESULTS: Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments. CONCLUSIONS: We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.W.D. Armstrong Trust Fund, CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066], The Mark Foundation for Cancer Research [C9685], the Austrian Science Fund [J-4025], National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre [BRC-1215-20014]

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

BackgroundHigh-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.MethodsIn this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.ResultsFive patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.ConclusionsWe developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.W.D. Armstrong Trust Fund, CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066], The Mark Foundation for Cancer Research [C9685], the Austrian Science Fund [J-4025], National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre [BRC-1215-20014]