Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization

Use of quantitative pathology to improve grading and predict prognosis in tumours of the gastrointestinal tract

Cancer represents a formidable health burden and was the second leading cause of death globally in 2018. In Norway, almost 35000 new cancer cases were reported in 2019. For colon cancer, the incidence and mortality rates in Norway are among the highest in the world. Furthermore, the tumour-node-metastasis (TNM) system used today is not optimal for selecting which patients should receive adjuvant therapy or not. With the implementation of digital pathology in different pathology departments, there will be better opportunities for digital image analysis, a tool aimed at giving a more reproducible and objective diagnosis than subjective evaluation in a microscope. In digital image analysis, a computer programme is used for the quantification of different biomarkers. This can improve cancer diagnostics because several biases in manual evaluation can be reduced or avoided. One of the challenges in pathology is intra-and inter-observer variability of prognostic and predictive biomarkers. This especially applies for gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs), in which the proliferation marker Ki67 is important for grading (1–3), prognosis and treatment of patients. Several studies have shown interand intra-observer variations in the manual evaluation of Ki67 positivity, which can be improved with digital image analysis. This is important because the interpretation of the immunohistochemical staining of different biomarkers, such as Ki67, often influences patient prognosis and treatment. The immune system, especially the number of T-cells in and around the tumour, has been investigated as a promising biomarker for predicting prognosis and survival in colorectal cancer (CRC). The immune system is closely linked to microsatellite instability (MSI) in CRC, and MSI-high CRC has been shown to respond well to immune therapy. A TNM-immune is suggested based on scoring of the number of T-cells in the tumour centre and the invasive margin using digital image analysis. In this study, we explored the correlation between T-cells in presurgical blood samples and T-cells in the invasive margins and the tumour centres in CRC with digital image analysis in a feasibility study and found a correlation. Furthermore, we used digital image analysis to calculate the immune score in colon cancer patients based on immunohistochemical (IHC) staining of cluster of differentiation (CD)3+ and CD8+ T-cells in invasive margins and tumour centres in a prospective cohort. This immune score corresponded strongly with known clinicopathological features, such as stage and MSI status. Also, we evaluated digital image analysis as an objective assessment tool for two different proliferation markers in GEP-NENs: Ki67 and Phosphohistone 3 (PHH3). We compared manual (visual) evaluation of Ki67 from pathology reports with digital image analysis of Ki67 and found excellent agreement, but there is a tendency to upgrade cases from grade 1 to grade 2 with digital image analysis. For the digital image analysis of PHH3, the measurements were more diverging. The data presented show the use of digital image analysis in two settings: developing an immune score as a prognostic marker in colon cancer and providing an objective and reproducible evaluation of proliferation in neuroendocrine neoplasms. With the transition to digital pathology, digital image analysis can be implemented in daily diagnostics. This implementation requires more research for the validation of the different methods. With time, digital image analysis is expected to be utilized for tasks performed by pathologists today.Doktorgradsavhandlin

Virtual biopsy in abdominal pathology: where do we stand?

In recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient’s phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits

Circulating hsa-miR-5096 predicts 18F-FDG PET/CT positivity and modulates somatostatin receptor 2 expression: a novel miR-based assay for pancreatic neuroendocrine tumors

Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) are rare diseases encompassing pancreatic (PanNETs) and ileal NETs (SINETs), characterized by heterogeneous somatostatin receptors (SSTRs) expression. Treatments for inoperable GEP-NETs are limited, and SSTR-targeted Peptide Receptor Radionuclide Therapy (PRRT) achieves variable responses. Prognostic biomarkers for the management of GEP-NET patients are required. 18F-FDG uptake is a prognostic indicator of aggressiveness in GEP-NETs. This study aims to identify circulating and measurable prognostic miRNAs associated with 18FFDG- PET/CT status, higher risk and lower response to PRRT

Multi-omics molecular profiling of lung tumours

Lung Cancer (LC) is one of the most common malignancies and is the leading cause of cancer death worldwide among both men and women. Current LC classifications are based on histopathological features which poorly reflect the molecular diversity of these tumours. Consequently, primary and secondary drug resistance are very frequent, and a high mortality is usual in LC patients. Despite the fact that LC has been intensively studied, there is a lack of effective biomarkers for early detection, stratification and prognosis. Integration of omics data is a powerful approach that can be used to identify molecular subgroups relevant in the clinical setting. This thesis addresses this challenge by characterising the molecular alterations accompanying LC at the genetic and DNA methylation level, using a combination of Whole-Exome Sequencing (WES), Targeted Capture Sequencing (TCS), Single Nucleotide Polymorphism (SNP) genotyping, Whole-Genome Bisulfite Sequencing and RNA-sequencing. The integration of different types of omics data first validated previous molecular alterations in frequently diagnosed LC tumours. This allowed comparison of the genomic and epigenomic landscapes between these common and rarer LC subtypes. Next, novel molecular subgroups of Non-Small Cell Lung Cancer (NSCLC) tumours with bad prognostic, as well as subgroups of Lung Carcinoids (L-CDs, an understudied LC subtype) have been identified and their molecular alterations and signatures characterised. Significant associations with histological features and gene expression programmes have been found by using several bioinformatic tools. These results show the value of multi-omics approaches to better understand the molecular mechanisms underlying LC and to identify new biomarkers. Importantly, some of these findings may be translatable and are likely to improve the detection, monitoring and stratification for targeted therapies in LC patients.Open Acces

Applications of machine learning algorithms using texture analysis-derived features extracted from computed tomography and magnetic resonance images

Radiomics relies on post-processing images derived from diagnostic examinations such as ultrasound, computed tomography (CT), magnetic resonance (MR) or positron emission tomography, by means of appropriate created algorithms with the extraction of a big amount of data. One of the main applications of radiomics is texture analysis (TA), a post processing imaging technique that analyzes the spatial variation of pixel intensity levels within an image obtaining quantitative data reflecting image heterogeneity. Machine learning (ML) is an application of artificial intelligence for recognizing patterns that can be applied to medical images, enabling the development of algorithms that can learn and make prediction. The aim of the present work is to illustrate our experience in TA and ML field using MR and CT images acquired in patients with adrenal lesions and head and neck cancer imaging, respectively. In particular, we aimed to assess the accuracy of ML algorithms in the differential diagnosis of adrenal lesions and to predict tumor grade and nodal involvement in oropharynx and oral cavity squamocellular carcinoma using MR and CT images, respectively. According to our results, the ML algorithm using MR-derived texture features correctly classified the 80% of adrenal lesions, performing better than a senior radiologist. When applied to CT-derived texture features, the ML classifier was also useful to accurately predict tumor grade, the presence of nodal involvement and to define N stage in patients with OC and OP SCC with a diagnostic accuracy of 91.6%, 85.5% and 90%, respectively Our results support the potential use of ML software employing TA-derived features for the differential diagnosis of solid lesions as well as for the prediction of histological features and the presence of nodal metastases in oncologic patients. The proven potential of ML to provide quantitative imaging biomarkers as well as the fast development of this technique will probably lead to its clinical implementation in radiological practice

Machine Learning Approaches to Predict Recurrence of Aggressive Tumors

Cancer recurrence is the major cause of cancer mortality. Despite tremendous research efforts, there is a dearth of biomarkers that reliably predict risk of cancer recurrence. Currently available biomarkers and tools in the clinic have limited usefulness to accurately identify patients with a higher risk of recurrence. Consequently, cancer patients suffer either from under- or over- treatment. Recent advances in machine learning and image analysis have facilitated development of techniques that translate digital images of tumors into rich source of new data. Leveraging these computational advances, my work addresses the unmet need to find risk-predictive biomarkers for Triple Negative Breast Cancer (TNBC), Ductal Carcinoma in-situ (DCIS), and Pancreatic Neuroendocrine Tumors (PanNETs). I have developed unique, clinically facile, models that determine the risk of recurrence, either local, invasive, or metastatic in these tumors. All models employ hematoxylin and eosin (H&E) stained digitized images of patient tumor samples as the primary source of data. The TNBC (n=322) models identified unique signatures from a panel of 133 protein biomarkers, relevant to breast cancer, to predict site of metastasis (brain, lung, liver, or bone) for TNBC patients. Even our least significant model (bone metastasis) offered superior prognostic value than clinopathological variables (Hazard Ratio [HR] of 5.123 vs. 1.397 p\u3c0.05). A second model predicted 10-year recurrence risk, in women with DCIS treated with breast conserving surgery, by identifying prognostically relevant features of tumor architecture from digitized H&E slides (n=344), using a novel two-step classification approach. In the validation cohort, our DCIS model provided a significantly higher HR (6.39) versus any clinopathological marker (p\u3c0.05). The third model is a deep-learning based, multi-label (annotation followed by metastasis association), whole slide image analysis pipeline (n=90) that identified a PanNET high risk group with over an 8x higher risk of metastasis (versus the low risk group p\u3c0.05), regardless of cofounding clinical variables. These machine-learning based models may guide treatment decisions and demonstrate proof-of-principle that computational pathology has tremendous clinical utility