A low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detection

© Springer Nature Switzerland AG 2019. In this paper, we aim to develop a low-computational system for real-time image processing and analysis in endoscopy images for the early detection of the human esophageal adenocarcinoma and colorectal cancer. Rich statistical features are used to train an improved machine-learning algorithm. Our algorithm can achieve a real-time classification of malign and benign cancer tumours with a significantly improved detection precision compared to the classical HOG method as a reference when it is implemented on real time embedded system NVIDIA TX2 platform. Our approach can help to avoid unnecessary biopsies for patients and reduce the over diagnosis of clinically insignificant cancers in the future.Published versio

Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: Present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review

Background: The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world.Main text: Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted.Conclusion: AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care

Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey

Since signet ring cells (SRCs) are associated with high peripheral metastasis rate and dismal survival, they play an important role in determining surgical approaches and prognosis, while they are easily missed by even experienced pathologists. Although automatic diagnosis SRCs based on deep learning has received increasing attention to assist pathologists in improving the diagnostic efficiency and accuracy, the existing works have not been systematically overviewed, which hindered the evaluation of the gap between algorithms and clinical applications. In this paper, we provide a survey on SRC analysis driven by deep learning from 2008 to August 2023. Specifically, the biological characteristics of SRCs and the challenges of automatic identification are systemically summarized. Then, the representative algorithms are analyzed and compared via dividing them into classification, detection, and segmentation. Finally, for comprehensive consideration to the performance of existing methods and the requirements for clinical assistance, we discuss the open issues and future trends of SRC analysis. The retrospect research will help researchers in the related fields, particularly for who without medical science background not only to clearly find the outline of SRC analysis, but also gain the prospect of intelligent diagnosis, resulting in accelerating the practice and application of intelligent algorithms

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202

Body composition changes over three years in older adults: a descriptive longitudinal analysis

Este trabalho foi financiado pelo Concurso Anual para Projetos de Investigação, Desenvolvimento, Inovação e Criação Artística (IDI&CA) 2016 do Instituto Politécnico de Lisboa. Código de referência: IPL/2016/SFQ2017_ESTeSLIntroduction: Many studies analyze body composition changes in older adults. However, few studies analyze body composition in elderly people with functional measures. Studies using Double X-Ray analysis (DXA) or Bioimpedance analysis proved to be reliable but expensive or only possible in a laboratory environment. The purpose of our study was to analyze changes in body composition over three years using anthropometric measures in a sample of elderly people in order to perceive functional changes. Methods: Forty-three participants (12 men; 31 women) aged 60 years and over and independent in activities of daily life were assessed using anthropometric measures in the first moment and past three years. Weight, height, waist and hip circumference were measured, and body mass index (BMI) and waist-to-hip ratio (WHR) were also calculated. Skeletal muscle mass (SMM) was also calculated using Al-Gindan et al. (2014) equations and normalized for height to found skeletal muscle index (SMI) in order to analyze cut-off points associated with physical disability according to Janssen et al (2004). Results: A significant difference was found over three years in SMM (p = 0.007), SMI (p = 0.027), BMI (p = 0.041) and WHR (p = 0.003). The majority of the participants has decreased SMM, SMI, and BMI and increased WHR, which favors a worst prognostic for comorbidities associated with these variables, and a tendency for sarcopenic obesity seems to be present although more studies are needed. Also, we found that using cut-off points for disability risk 83.3% of the men and 38.7% of the women of our sample were at moderate or high risk of disability. Three years later this percentage has increased but only for women to 54.8%. Conclusions: Although men are at risk of disability, women quickly lose their functional capacity, making necessary a rapid intervention to reduce the risk of disability in this population.info:eu-repo/semantics/publishedVersio

Outcomes of MR-guided Stereotactic Body Radiotherapy (SBRT) or yttrium-90 Transarterial Radioembolization for Hepatocellular Carcinoma Treated at an Urban Liver Transplant Center

Background: There are overlapping indications for both stereotactic body radiotherapy (SBRT) and yttrium-90 (Y90) trans-arterial radioembolization as locoregional treatments for hepatocellular cancer, though most centers preferentially use one modality over the other. MR-guided radiation allows both effective on-table localization and integrated motion management as compared with many traditional linear accelerators, allowing SBRT to be done more easily. Y90 radioembolization has been a well-established modality to deliver highly conformal dose due to the localization of the microspheres to the vascular supply of a tumor. We looked at patient characteristics and treatment outcomes for patients receiving MR-guided SBRT or Y90 at an urban transplant center. Objectives: To compare patient characteristics and treatment outcomes of MR-guided SBRT with Y90 transarterial radioembolization in a liver transplant center. Methods: This retrospective single-institution study analyzed patients with HCC treated with SBRT or Y90 from August 2017 to September 2020. To select a patient population eligible for either treatment modality, any Y90 procedures for lesions \u3e 10 cm or for treatment volumes \u3e 1000 cc were omitted from the cohort. A total of 239 patients were included in the analysis, receiving a total of 98 courses of SBRT and 187 courses of Y90 treatment. Local control (LC), freedom from liver progression (FFLP), and overall survival (OS) rates were measured from treatment completion date to death date or last follow-up. All outcomes were censored at time of loss to follow-up; LC and FFLP were censored at time of liver transplant if applicable. Cox regression models were used for survival, with significant factors on the univariate analysis further analyzed with a multivariate model. Results: Median time to follow-up was 11 months (0-44 mo). The mean size of lesions treated with SBRT were smaller than those treated with Y90 (2.7 cm vs 4.3 cm, P\u3c0.01). The groups of patients differed in liver disease characteristics, with SBRT patients having fewer Child-Pugh A disease (62% vs 80%, P\u3c0.01), more having received locoregional treatments to the liver in the past (81% v 35%, P\u3c0.01), and more disease in previously treated liver (57% vs 25%, P\u3c0.01). Dose of radiation for SBRT was 45-50 Gy administered in 5 fractions; dose of Y90 radiation to tumor was prescribed to a median of 235.2 Gy (range 55.8-512.3 Gy). There was a higher rate of one year LC in the SBRT cohort (77% vs 57%, P\u3c0.01), while median FFLP (9 mo vs 8 mo, P=NS) and median OS were not significantly different (24 mo vs 21 mo, P=NS). Multivariate analysis revealed size of largest lesion (P\u3c0.01) was correlated with decreased local control; a 1 cm increase in tumor size was associated with a 25% increased risk of local failure. Subsequent transplant (P\u3c0.01) was the remaining significant factor. Treatment modality did not remain an independent predictor of LC. Predictors of OS in multivariate analysis included age (P=0.01), prior liver treatments (HR 2.86, P\u3c0.01), size of largest lesion (P\u3c0.01), Child-Pugh stage (P\u3c0.01), portal vein thrombosis (HR 1.6, P=0.04), and subsequent liver transplant (HR 0.08, P\u3c0.01). Conclusions: These findings support the effectiveness of both MR-guided SBRT and Y90 transarterial radioembolization in locoregional management of HCC at a single institution despite clear differences in the patient cohorts. Though survival outcomes were comparable, local control differences favored the cohort treated by SBRT, in large part due to differences in tumor size. This data supports further investigation in a randomized study between SBRT and Y90

Survival Outcomes and Patterns of Recurrence After Adjuvant Vaginal Cuff Brachytherapy and Chemotherapy in Early-Stage Uterine Serous Carcinoma

Background: Uterine serous carcinoma (USC) is a relatively rare histology that portends a poor prognosis. The optimal adjuvant therapy for early-stage USC remains controversial; however, adjuvant vaginal cuff brachytherapy (VB) and chemotherapy is a commonly utilized strategy. Objectives: We sought to characterize predictors of survival endpoints and determine recurrence patterns in women with early-stage USC who received adjuvant VB and chemotherapy. Methods: We queried our prospectively maintained database for patients with 2009 FIGO stages I-II USC who underwent adequate surgical staging at our institution and received adjuvant chemotherapy with carboplatin and paclitaxel along with VB. We excluded women with synchronous malignancies. Overall survival (OS), disease-specific survival (DSS), and recurrence-free survival (RFS) were assessed by Kaplan-Meier and log-rank tests. Univariate (UVA) and multivariate analyses (MVA) were performed to identify statistically significant predictors of survival endpoints. Variables with P\u3c0.1 on UVA were included in a MVA and any variable with P\u3c0.05 was considered statistically significant. Results: We identified 77 women who met our inclusion criteria who underwent surgical staging between 1991 and 2018. The median follow-up time was 36 months (range 6-125). The median age was 66 years. Of the cohort, 70% were FIGO stage IA, 17% were stage IB, and 13% were stage II. The median number of dissected lymph nodes (LN) was 22. There were 10 women (13%) diagnosed with a recurrence with a median time to recurrence of 12.0 months. The main site of initial recurrence was distant in seven patients (70%) with the remaining recurrences being pelvic/para-aortic. The 5-year RFS for patients who experienced a distant recurrence was 87% (95% Confidence Interval [CI] 0.75-0.94). For the entire cohort, 5-year OS, DSS, and RFS were 83% (95% CI 0.68-0.91), 92% (95% CI 0.78-0.97), and 83% (95% CI 0.71-0.91), respectively. The sole predictor of 5-year OS on UVA was receipt of omentectomy (P=0.09). The predictors of 5-year DSS on UVA were presence of positive peritoneal cytology (P=0.03), number of LN examined (Hazard Ratio [HR] 1.10, 95% CI 1.00-1.21, P=0.05), and number of para-aortic LN examined (HR 1.16 [95% CI 1.01-1.32], P=0.03). The sole independent predictor of DSS was the presence of positive peritoneal cytology (HR 0.03 [95% CI 0.00-0.72], P=0.03). Predictors of five-year RFS on UVA were robotic vs open surgery technique (P=0.06), presence of positive peritoneal cytology (P=0.01), percent myometrial invasion (HR 5.59 [95% CI 0.84-37.46], P=0.08), and presence of lymphovascular space invasion (LVSI) (P=0.05). Conclusions: Five-year survival outcomes were promising in this cohort of women with early-stage USC treated with adjuvant chemotherapy and VB; however, this study shows that the predominant pattern of relapse in this population is distant, suggesting the need to optimize systemic therapy. Possible predictors of worse outcomes include positive peritoneal cytology, deep myometrial invasion, and presence of LVSI. Multi-institutional pooled analyses are warranted to confirm our study results