Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathological Predictive Power of RhoB in Rectal Cancer Biopsy

RhoB protein belongs to the Rho GTPase family, which plays an important role in governing cell signaling and tissue morphology. RhoB expression is known to have implications in pathological processes of diseases. Investigation in the regulation and communication of this protein detected by immunohistochemical staining on the microscope is worth exploring to gain insightful information that may lead to identifying optimal disease treatment options. In particular, the role of RhoB in rectal cancer is not well-discovered. Here, we report that methods of deep learning-based image analysis and the decomposition of multiway arrays discover the predictive factor of RhoB in two cohorts of rectal-cancer patients having survival rates of less and more than 5 years. The analysis results show distinctions between the tensor-decomposition factors of the two cohorts.

EP.TH.322Should a concurrent sentinel node biopsy be performed when the preoperative histopathological category of B5c in a breast cancer biopsy is identified?

Aim To assess the management of sentinel lymph node biopsy (SLNB) in patients with a pre-operative B5c histopathological diagnosis. Method A retrospective study of the decision for SLNB in 31 breast cancer patients with B5c biopsy and their final histology were reviewed between 2009-2019. Factors that determine concurrent SLNB (size of mass lesion > 1cm/increasing size, DCIS > 4cm, recurrence and abnormal lymph nodes on ultrasound) were assessed. Results 15 patients (48%) with invasive disease and 7 patients (23%) with non-invasive disease had concurrent SLNB, whilst 9 (29%) patients with non-invasive disease did not receive concurrent SLNB and no patient (0%) with invasive disease failed to receive concurrent SLNB. Using the above inclusion and exclusion criteria 21 of 31 (68%) patients would have been managed correctly whilst 5 (16%) would of had unnecessary surgery and 5 (16%) would have required a second operation. Discussion In this study, 48% of patients with concurrent SLNB were identified as invasive and avoided a second operation. Although 23% of patients underwent unnecessary SNLB, no invasive disease SLNBs were missed. Solely using inclusion and exclusion criteria would not have improved results. This suggests that there must be other factors involved in correctly managing these patients. Conclusion Discussions with the patient about risk benefits of SLN complications vs repeat surgery, and patient comorbidity, in addition to radiological characteristics, may assist management decisions.

Design of an Ultrasound-Navigated Prostate Cancer Biopsy System for Nationwide Implementation in Senegal

This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on “frugal technology”, intended to be affordable for low–middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.

Multinomial Classification of Patterns in Lung Cancer Biopsy Slides Using Customized Convolutional Neural Network

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma (adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell neuroendocrine carcinoma) and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We evaluated the diagnostic performance of each model in the test sets. The Xception model achieved the highest performance among pre-trained CNNs with an accuracy of 0.86 and an area under the curve (AUC) of 0.97. The built from scratch CNN model obtained an accuracy of 0.92 and an AUC ranging from 0.99 to 1.00 for subtyping lung carcinoma tasks. These results demonstrate how promising CNN models are for developing improved diagnostic workflow systems for diagnosis and subtyping of lung carcinoma. Of particular note is the fact that the built from scratch CNN described in this paper achieves prompt and consistent results so has the potential to be applied in working hospitals for pathological diagnoses.

Restructuring Skin Cancer Care in Ontario: A Provincial Plan

There is a global rise in skin cancer incidence, resulting in an increase in patient care needs and healthcare costs. To optimize health care planning, costs, and patient care, Ontario Health developed a provincial skin cancer plan to streamline the quality of care. We conducted a systematic review and a grey literature search to evaluate the definitions and management of skin cancer within other jurisdictions, as well as a provincial survey of skin cancer care practices, to identify care gaps. The systematic review did not identify any published comprehensive skin cancer management plans. The grey literature search revealed skin cancer plans in isolated regions of the United Kingdom (U.K.), National Institute for Health and Care Excellence (NICE) guidelines for skin cancer quality indicators and regional skin cancer biopsy clinics, and wait time guidelines in Australia and the U.K. With the input of the Ontario Cancer Advisory Committee (CAC), unique definitions for complex and non-complex skin cancers and the appropriate cancer services were created. A provincial survey of skin cancer care yielded 44 responses and demonstrated gaps in biopsy access. A skin cancer pathway map was created and a recommendation was made for regional skin cancer biopsy clinics. We have created unique definitions for complex and non-complex skin cancer and a skin cancer pathways map, which will allow for the implementation of both process and performance metrics to address identified gaps in care.