Search

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.

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.