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Breast cancer (BCa) and prostate cancer (PCa) are the most prevalent types of cancers. We aimed to understand and analyze the care pathways for BCa and PCa patients followed at a hospital setting by analyzing their different treatment lines. We evaluated the association between different treatment lines and the lifestyle and demographic characteristics of these patients. Two datasets were created using the electronic health records (EHRs) and information collected through semi-structured one-on-one interviews. Statistical analysis was performed to examine which variable had an impact on the treatment each patient followed. In total, 83 patients participated in the study that ran between January and November 2018 in Beacon Hospital. Results show that chemotherapy cycles indicate if a patient would have other treatments, i.e., patients who have targeted therapy (25/46) have more chemotherapy cycles (95% CI 4.66–9.52, p = 0.012), the same is observed with endocrine therapy (95% CI 4.77–13.59, p = 0.044). Patients who had bisphosphonate (11/46), an indication of bone metastasis, had more chemotherapy cycles (95% CI 5.19–6.60, p = 0.012). PCa patients with tall height (95% CI 176.70–183.85, p = 0.005), heavier (95% CI 85.80–99.57, p &lt, 0.001), and a BMI above 25 (95% CI 1.85–2.62, p = 0.017) had chemotherapy compared to patients who were shorter, lighter and with BMI less than 25. Initial prostate-specific antigen level (PSA level) indicated if a patient would be treated with bisphosphonate or not (95% CI 45.51–96.14, p = 0.002). Lifestyle variables such as diet (95% CI 1.46–1.85, p = 0.016), and exercise (95% CI 1.20–1.96, p = 0.029) indicated that healthier and active BCa patients had undergone surgeries. Our findings show that chemotherapy cycles and lifestyle for BCa, and tallness and weight for PCa may indicate the rest of treatment plan for these patients. Understanding factors that influence care pathways allow a more person-centered care approach and the redesign of care processes.

Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date.

Colorectal cancer is a leading cause of cancer deaths, estimated 696 thousand worldwide. Recent years have seen an increase of deep learning techniques and algorithms being used to detect colon polyps. In this work we address colon polyp detection using Convolutional Neural Networks (CNNs) combined with Autoencoders. We use 3 publicly available databases namely: CVC-ColonDB, CVC-ClinicDB and ETIS-LaribPolypDB, to train the model. The results obtained in terms of accuracy are: 0.937, 0.951, 0.967 for the above-mentioned databases respectively. Due to the nature of the colon polyps, diverse shapes and characteristics, there is still place for improvements.

Background: The implementation of Integrated Care poses many challenges at a macro, meso and micro level. This workshop aims to explore the role that a proven intervention like Integrated Care Pathways is playing in advancing cultural and organizational transformation towards integrated care. Worldwide there are a plethora of ICPs implementation initiatives offering a great opportunity for knowledge exchange and cross-boundaries learning, but the latest studies show a high degree of variability in the implementation of ICPs. Aims and Objectives: The Aim of this workshop is to offer the ICIC 2019 audience an opportunity to hear from the latest international advances on ICPs implementation and to reflect upon collectively. Specific objectives are: - To share the state-of-the-art evidence on ICPs - To present the latest analysis of ICPs implementation, variability, challenges and key learnings - To present key findings from a best practice intervention of ICPs implementation at a systemic level in Spain - To present key findings and understandings from an ICPs case study with cancer patients at a hospital in Ireland - To access an innovative example of ICPs technology-based implementation in cancer patients - To debate on the future role of research in ICPS as a technology to promote Integrated Care and inform practice - To inspire reflection on the crossroads between ICPs and Chronic Care, Population Health Management and healthcare prevention - To allow discussion on effective ways to engage patients participation in ICPs developments Format: The proposed format for the workshop consists of a 60 minutes session as part of the main conference stream. The workshop intends to be a step in the ongoing process of reflection on ICPs research, implementation improvement, performance analysis and patient engagement. Timings and speakers: 15 minutes. Massimiliano Panella (Italy). President of the European Pathway Association. Latest international evidence on ICPs implementation. 15 minutes. Carles Blay (Spain). Family Physician, ex-director of ICP strategy in Catalunya and Director of Medicine Faculty at Vic University. Key learnings from ICPs implementation in Spain 15 minutes. Ornela Bardhi (Albania). Marie Curie PhD Fellow at Cancer: Activating Technology for Connected Health. Understanding and mapping the Patient Journey in Cancer. 15 minutes. Open debate and conclusions. Coordinated by Elena Urizar, Deusto Business School Health. Target audience: Healthcare organization managers, policy makers, decision makers and implementation teams and researchers in the field of ICPs and integrated care. Learnings/Take away: The output of the roundtable will be relevant for informing research and practice on key aspects to improve ICPs implementation and on finding ways to include patient’s participation in the development of ICPs.

Colorectal cancer is a leading cause of cancer deaths, estimated 696 thousand worldwide. Recent years have seen an increase of deep learning techniques and algorithms being used to detect colon polyps. In this work we address colon polyp detection using Convolutional Neural Networks (CNNs) combined with Autoencoders. We use 3 publicly available databases namely: CVC-ColonDB, CVC-ClinicDB and ETIS-LaribPolypDB, to train the model. The results obtained in terms of accuracy are: 0.937, 0.951, 0.967 for the above-mentioned databases respectively. Due to the nature of the colon polyps, diverse shapes and characteristics, there is still place for improvements.