Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast

IntroductionWe previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm.Materials and MethodsIn this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The “positive class” was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases.ResultsArea under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively.ConclusionProspectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity.     

PBL与思维导图相结合教学模式在临床教学中的应用

目的探讨PBL与思维导图相结合教学模式在临床教学中的应用。方法本文研究对象为在我院见习的临床本科生,为大理大学2015级一个班级,共55名学生,数据收集时间为2019年1月;按照授课方式的差异将学生分为实验班与对照班,实验班27名学生,对照班28名学生;对照班实施常规教学法,实验班实施PBL与思维导图相结合教学法。结果实验班教学结束测试得分为(85.60±3.46)分,对照班教学结束测试得分为(77.48±3.12)分,差异具有统计学意义(P<0.05);实验班教学方式满意度评分为(90.35±4.12)分,对照班教学方式满意度评分为(80.25±4.05)分,差异具有统计学意义(P<0.05)。结论在临床教学中采用PBL与思维导图相结合教学模式,能够提高学生的专业知识掌握水平与对教学模式的认可度。     

Easy Words: Reference Resolution in a Malevolent Referent World

This article describes early stages in the acquisition of a first vocabulary by infants and young children. It distinguishes two major stages, the first of which operates by a stand‐alone word‐to‐world pairing procedure and the second of which, using the evidence so acquired, builds a domain‐specific syntax‐sensitive structure‐to‐world pairing procedure. As we show, the first stage of learning is slow, restricted in character, and to some extent errorful, whereas the second procedure is determinative, rapid, and essentially errorless. Our central claim here is that the early, referentially based learning procedure succeeds at all because it is reined in by attention‐focusing properties of word‐to‐world timing and related indicants of referential intent.     

Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility

Absorption of drugs is the first step after dosing, and it largely affects drug bioavailability. Hence, estimating the fraction of absorption (Fa) in humans is important in the early stages of drug discovery. To achieve correct exclusion of low Fa compounds and retention of potential compounds, we developed a freely available model to classify compounds into 3 levels of Fa capacity using only the chemical structure. To improve Fa prediction, we added predicted binary classification results of membrane permeability measured using Caco-2 cell line (Papp) and dried–dimethyl sulfoxide solubility (accuracy, 0.836; kappa, 0.560). The constructed models can be accessed via a web application.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.