Radiomics analysis in gastrointestinal imaging: a narrative review

Background and Objective: To present an overview of radiomics radiological applications in major gastrointestinal oncological non-oncologic diseases, such as colorectal cancer, pancreatic cancer, gastro- oesophageal cancer, gastrointestinal stromal tumor (GIST), hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and non-oncologic diseases, such as liver fibrosis, nonalcoholic steatohepatitis, and inflammatory bowel disease. Methods: A search of PubMed databases was performed for the terms “radiomic”, “radiomics”, “liver”, “small bowel”, “colon”, “GI tract”, and “gastrointestinal imaging” for English articles published between January 2013 and July 2022. A narrative review was undertaken to summarize literature pertaining to application of radiomics in major oncological and non-oncological gastrointestinal diseases. The strengths and limitation of radiomics, as well as advantages and major limitations and providing considerations for future development of radiomics were discussed. Key Content and Findings: Radiomics consists in extracting and analyzing a vast amount of quantitative features from medical datasets, Radiomics refers to the extraction and analysis of large amounts of quantitative features from medical images. The extraction of these data, integrated with clinical data, allows the construction of descriptive and predictive models that can build disease-specific radiomic signatures. Texture analysis has emerged as one of the most important biomarkers able to assess tumor heterogeneity and can provide microscopic image information that cannot be identified with the naked eye by radiologists. Conclusions: Radiomics and texture analysis are currently under active investigation in several institutions worldwide, this approach is being tested in a multitude of anatomical areas and diseases, with the final aim to exploit personalized medicine in diagnosis, treatment planning, and prediction of outcomes. Despite promising initial results, the implementation of radiomics is still hampered by some limitations related to the lack of standardization and validation of image acquisition protocols, feature segmentation, data extraction, processing, and analysi

Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study

Background: The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making process. With the advent of Artificial intelligence (AI) techniques, various omics datasets may be used to create more accurate predictive models paving the way for a better care in lung cancer patients. Methods: The LANTERN study is a multi-center observational clinical trial involving a multidisciplinary consortium of five institutions from different European countries. The aim of this trial is to develop accurate several predictive models for lung cancer patients, through the creation of Digital Human Avatars (DHA), defined as digital representations of patients using various omics-based variables and integrating well-established clinical factors with genomic data, quantitative imaging data etc. A total of 600 lung cancer patients will be prospectively enrolled by the recruiting centers and multi-omics data will be collected. Data will then be modelled and parameterized in an experimental context of cutting-edge big data analysis. All data variables will be recorded according to a shared common ontology based on variable-specific domains in order to enhance their direct actionability. An exploratory analysis will then initiate the biomarker identification process. The second phase of the project will focus on creating multiple multivariate models trained though advanced machine learning (ML) and AI techniques for the specific areas of interest. Finally, the developed models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the DHA. All the potential clinical and scientific stakeholders will be involved in the DHA development process. The main goals aim of LANTERN project are: i) To develop predictive models for lung cancer diagnosis and histological characterization; (ii) to set up personalized predictive models for individual-specific treatments; iii) to enable feedback data loops for preventive healthcare strategies and quality of life management. Discussion: The LANTERN project will develop a predictive platform based on integration of multi-omics data. This will enhance the generation of important and valuable information assets, in order to identify new biomarkers that can be used for early detection, improved tumor diagnosis and personalization of treatment protocols. Ethics Committee approval number: 5420 − 0002485/23 from Fondazione Policlinico Universitario Agostino Gemelli IRCCS – Università Cattolica del Sacro Cuore Ethics Committee. Trial registration: clinicaltrial.gov - NCT05802771

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis

Thyroid cartilage infiltration in advanced laryngeal cancer: prognostic implications and predictive modelling

Objective: Detection of laryngeal cartilage invasion is of great importance in staging of laryngeal squamous cell carcinoma (LSCC). The role of prognosticators in locally advanced laryngeal cancer are still widely debated. This study aimed to assess the impact of volume of thyroid cartilage infiltration, as well as other histopathologic variables, on patient survival. Materials and methods: We retrospectively analysed 74 patients affected by pT4 LSCC and treated with total laryngectomy between 2005 and 2021 at the Department of Otorhinolaryngology - Head and Neck Surgery of the University of Brescia, Italy. We considered as potential prognosticators histological grade, perineural (PNI) and lympho-vascular invasion (LVI), thyroid cartilage infiltration, and pTN staging. Pre-operative CT or MRI were analysed to quantify the volume of cartilage infiltration using 3D Slicer software. Results: The 1-, 3-, and 5-year disease free survivals (DFS) were 76%, 66%, and 64%, respectively. Using machine learning models, we found that the volume of thyroid cartilage infiltration had high correlation with DFS. Patients with a higher volume (> 670 mm3) of infiltration had a worse prognosis compared to those with a lower volume. Conclusions: Our study confirms the essential role of LVI as prognosticator in advanced LSCC and, more innovatively, highlights the volume of thyroid cartilage infiltration as another promising prognostic factor

Quantification of Coronary Artery Atherosclerotic Burden and Muscle Mass: Exploratory Comparison of Two Freely Available Software Programs

Abstract: Coronary artery calcification and sarcopenia may have a relevant prognostic impact in oncological and non‐oncological patients. The use of freeware software is promising for quantitative evaluation of these parameters after whole‐body positron emission tomography (PET)/computed tomography (CT) and might be useful for one‐stop shop risk stratification without additional radiation ionizing burden and further charges to health care costs. In this study, we compared two semiautomatic freeware software tools (Horos Medical Image software and LIFEx) for the assessment of coronary artery calcium (CAC) score and muscle mass in 40 patients undergoing whole‐body PET/CT. The muscle areas obtained by the two software programs were comparable, showing high correlation with Lin’s concordance coefficient (0.9997; 95% confidence intervals: 0.9995–0.9999) and very good agreement with Bland–Altman analysis (mean difference = 0.41 cm2, lower limit = −1.06 cm2, upper limit = 1.89) was also found. For CAC score, Lin’s concordance correlation coefficient was 0.9976 (95% confidence intervals: 0.9965–0.9984) and in a Bland–Altman analysis an increasing mean difference from 8 to 78 by the mean values (intercept = −0.050; slope = 0.054; p < 0.001) was observed, with a slight overestimation of Horos CAC score as compared to LIFEx, likely due to a different calculation method of the CAC score, with the ROI being equal for the two software programs. Our results demonstrated that off‐line analysis performed with freeware software may allow a comprehensive evaluation of the oncological patient, making available the evaluation of parameters, such as muscle mass and calcium score, that may be relevant for the staging and prognostic stratification of these patients, beside standard data obtained by PET/CT imaging. For this purpose, the Horos and LIFEx software seem to be interchangeable