Developing image analysis methods for digital pathology

The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

While cross-sectional imaging has seen continuous progress and plays an undiscussedpivotal role in the diagnostic management and treatment planning of patients with rectal cancer, alargely unmet need remains for improved staging accuracy, assessment of treatment response andprediction of individual patient outcome. Moreover, the increasing availability of target therapies hascalled for developing reliable diagnostic tools for identifying potential responders and optimizingoverall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fullyevolving research topic, which could harness the power of modern computer technology to generatequantitative information from imaging datasets based on advanced data-driven biomathematicalmodels, potentially providing an added value to conventional imaging for improved patient manage-ment. The present study aimed to illustrate the contribution that current radiomics methods appliedto magnetic resonance imaging can offer to managing patients with rectal cancer

Desarrollo y aplicación de métodos bioinformáticos al análisis de datos de expresión genómica y datos de supervivencia de pacientes con cáncer

[EN] The development of robust omic technologies (genomics, transcriptomics, proteomics, etc) to generate and understand genome-wide alterations is already having an impact on health care, with a particular relevance on cancer and oncology. Within the current context of Personalised Medicine, Precision Medicine and Genomic Medicine (Roden and Tyndale, 2013), modern cancer research has to be done considering an adecuate use of the large-scale data derived from these new omic technologies. Some of these technologies, such as transcriptomic expression pro ling, have been already applied to thousands of human samples (see public database GEO (NCBI, 2019)), and provide information about the expression status of all the known genes in the analised individuals. In order to be useful and applicable to medical research, such omic data should be integrated with the corresponding clinical data using adequate computational and bioinformatic tools and methods. This is a main framework where the current Doctoral Thesis work is proposed

Prognostic gene expression signature associated with two molecularly distinct subtypes of colorectal cancer.

AIMS: Despite continual efforts to develop prognostic and predictive models of colorectal cancer by using clinicopathological and genetic parameters, a clinical test that can discriminate between patients with good or poor outcome after treatment has not been established. Thus, the authors aim to uncover subtypes of colorectal cancer that have distinct biological characteristics associated with prognosis and identify potential biomarkers that best reflect the biological and clinical characteristics of subtypes. METHODS: Unsupervised hierarchical clustering analysis was applied to gene expression data from 177 patients with colorectal cancer to determine a prognostic gene expression signature. Validation of the signature was sought in two independent patient groups. The association between the signature and prognosis of patients was assessed by Kaplan-Meier plots, log-rank tests and the Cox model. RESULTS: The authors identified a gene signature that was associated with overall survival and disease-free survival in 177 patients and validated in two independent cohorts of 213 patients. In multivariate analysis, the signature was an independent risk factor (HR 3.08; 95% CI 1.33 to 7.14; p=0.008 for overall survival). Subset analysis of patients with AJCC (American Joint Committee on Cancer) stage III cancer revealed that the signature can also identify the patients who have better outcome with adjuvant chemotherapy (CTX). Adjuvant chemotherapy significantly affected disease-free survival in patients in subtype B (3-year rate, 71.2% (CTX) vs 41.9% (no CTX); p=0.004). However, such benefit of adjuvant chemotherapy was not significant for patients in subtype A. CONCLUSION: The gene signature is an independent predictor of response to chemotherapy and clinical outcome in patients with colorectal cancer.ope