Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Radiation Response Biomarkers for Individualised Cancer Treatments

Personalised medicine is the next step in healthcare, especially when applied to genetically diverse diseases such as cancers. Naturally, a host of methods need to evolve alongside this, in order to allow the practice and implementation of individual treatment regimens. One of the major tasks for the development of personalised treatment of cancer is the identification and validation of a comprehensive, robust, and reliable panel of biomarkers that guide the clinicians to provide the best treatment to patients. This is indeed important with regards to radiotherapy; not only do biomarkers allow for the assessment of treatability, tumour response, and the radiosensitivity of healthy tissue of the treated patient. Furthermore, biomarkers should allow for the evaluation of the risks of developing adverse late effects as a result of radiotherapy such as second cancers and non-cancer effects, for example cardiovascular injury and cataract formation. Knowledge of all of these factors would allow for the development of a tailored radiation therapy regime. This Special Issue of the Journal of Personalised Medicine covers the topic of Radiation Response Biomarkers in the context of individualised cancer treatments, and offers an insight into some of the further evolution of radiation response biomarkers, their usefulness in guiding clinicians, and their application in radiation therapy

Serum proteomic analysis of prostate cancer progression

Background: The reported incidence of prostate cancer (PCa) has increased in recent years due to the aging of the population and increased testing; however mortality rates have remained largely unchanged. Studies have shown deficiencies in predicting patient outcome for both of the major PCa diagnostic tools, namely prostate specific antigen (PSA) and trans rectal ultrasound ‐guided biopsy (TRUS). Therefore, serum biomarkers are needed that accurately predict prognosis of PCa (indolent vs. aggressive) and can thus inform clinical management. Aim: This study uses surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI‐TOF‐MS) analysis to identify differential serum protein expression between PCa patients with indolent vs. aggressive disease categorised by Gleason grade and biochemical recurrence. Materials and Methods: A total of 99 serum samples were selected for analysis. According to Gleason score, indolent (45 samples) and aggressive (54) forms of PCa were compared using univariate analysis. The same samples were then separated into groups of different recurrence status (10 metastatic, 15 biochemical recurrence and 70 nonrecurrences) and subjected to univariate analysis in the same way. The data from Gleason score and recurrence groups were then analysed using multivariate statistical analysis to improve PCa biomarker classification. Using gel‐electrophoresis technique, candidate biomarkers were separated and identified by LC‐MS/MS and validated using optimised Western blot (WB) immunoassay against 100 PCa serum samples from the Wales Cancer Bank (50 as indolent group & 50 as aggressive group). Results: The comparison between serum protein spectra from indolent and aggressive samples resulted in the identification of twenty‐six differentially expressed protein peaks (p<0.05), of which twenty proteins were found with 99% confidence. A total of 18 differentially expressed proteins (p<0.05) were found to distinguish between recurrence groups; three of these were robust with P<0.01. Sensitivity and specificity within the Gleason score group was 73.3% and 60% respectively and for the recurrence group 70% and 62.5%. Four candidate biomarkers (categorised by Gleason score) were identified using a novel 1 D LC‐MS/MS technique. The candidate biomarker with m/z of 9.3 kDa was found to be upregulated in aggressive PCa patients, and was identified as Apolipoprotein C‐I (ApoC‐I). Another three candidate biomarkers (22.2, 44.5 and 79.1 kDa) were found downregulated in the aggressive group and up‐ regulated in the indolent group and identified as apolipoprotein D (ApoD), putative uncharacterised protein (PUP) and Transferrin (TF), respectively. The utility of the putative biomarkers was examined by Western blot (WB) analysis of 100 blinded PCa serum samples. None of the three SELDI identified biomarkers were able to statistically identify PCa patients’ progression. Conclusion: The use of SELDI to identify potential PCa progression biomarkers has been confirmed in PCa patients. However, immunovalidation of prospective biomarkers in blinded PCa serum samples was unsuccessful. This study demonstrates the importance of validation in ascertaining the true clinical applicability of a cancer biomarker.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Advances and Novel Treatment Options in Metastatic Melanoma

The book presents several studies reporting advances on melanoma pathogenesis, diagnosis and therapy. It represents a milestone on the state of the art, updated at 2021, and also presents the current knowledge on the future developments in melanoma field

Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation

In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid–base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes

Abstracts from the 50th European Society of Human Genetics Conference: Posters

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