Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Coupling online control and inhibitory systems in children with Developmental Coordination Disorder: Goal-directed reaching

For children with Developmental Coordination Disorder (DCD), the real-time coupling between frontal executive function and online motor control has not been explored despite reported deficits in each domain. The aim of the present study was to investigate how children with DCD enlist online control under task constraints that compel the need for inhibitory control. A total of 129 school children were sampled from mainstream primary schools. Forty-two children who met research criteria for DCD were compared with 87 typically developing controls on a modified double-jump reaching task. Children within each skill group were divided into three age bands: younger (6–7 years), mid-aged (8–9), and older (10–12). Online control was compared between groups as a function of trial type (non-jump, jump, anti-jump). Overall, results showed that while movement times were similar between skill groups under simple task constraints (non-jump), on perturbation (or jump) trials the DCD group were significantly slower than controls and corrected trajectories later. Critically, the DCD group was further disadvantaged by anti-jump trials where inhibitory control was required; however, this effect reduced with age. While coupling online control and executive systems is not well developed in younger and mid-aged children, there is evidence of age-appropriate coupling in older children. Longitudinal data are needed to clarify this intriguing finding. The theoretical and applied implications of these results are discussed.     

Training in Informatics: Teaching Informatics in Surgical Pathology

This article presents an overview of the curriculum deemed essential for trainees in pathology, with mapping to the Milestones competency statements. The means by which these competencies desired for pathology graduates, and ultimately practitioners, can best be achieved is discussed. The value of case (problem)-based learning in this realm, in particular the kind of integrative experience associated with hands-on projects, to both cement knowledge gained in the lecture hall or online and to expand competency is emphasized.     

基于剂量组学预测肺癌患者放射性肺炎发生的研究

目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法（bootstrap）的最小绝对值收敛和选择算子嵌套逻辑回归（LASSO‐LR）及1000次bootstrap的赤池信息量准则（AIC）向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77（95%CI为0.65~0.87）,独立验证集AUC为0.72（95%CI为0.64~0.81）。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。