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.     

中日友好医院分型股骨头坏死腓骨植入治疗的三维有限元分析

文题释义：股骨头坏死中日友好医院分型的有限元分析：根据李子荣等提出的中日友好医院分型，建立股骨头坏死三维模型，分为 M型(内侧型)、C型(中央型)和 L型(外侧型)，其中 L型包括L1型(次外侧型)、L2型(极外侧型)和 L3型(全头型)。通过对建立的模型进行有限元分析，为该分型的保髋治疗提供了一定力学依据，显示外侧柱的存留是精准预防塌陷的重要因素，为进一步实现个体化治疗提供力学基础。 腓骨支撑坏死股骨头保髋手术：是对于早中期股骨头坏死需要保留股骨头患者进行的一种手术方式。首先需对股骨头进行髓芯减压，清除一定坏死骨，空腔填塞松质骨(髂骨为主)，打压结实后植入腓骨(异体或自体)支撑，给坏死区的提供力学支撑及生物学修复，预防股骨头进一步坏死及塌陷。 背景：研究报道股骨头坏死的保髋疗效与外侧柱存留密切相关，中日友好医院分型是根据三柱结构确立的，对股骨头塌陷的预测准确性高。 目的：建立股骨头坏死中日友好医院分型各分型仿真的三维有限元模型，通过有限元分析各分型腓骨植入的力学变化，探讨外侧柱存留对保髋疗效的意义，为该分型的塌陷精准预测提供基础。 方法：建立正常股骨头、中日友好医院分型(M型、C型、L1型、L2型、L3型)股骨头坏死及其腓骨植入3组11种三维有限元模型，运用ANSYS软件进行有限元分析计算，观察各组模型的最大应力值、最大位移值及股骨头内部载荷传递模式。 结果与结论：①坏死组位移最大，应变最大，且因坏死分型不同而位移不同，位移变化如下：M型相似文献   

Tongue cancer treatment and oncological outcomes: The role of glossectomy classification

BackgroundNowadays surgery remains the gold standard of treatment for tongue cancer. Via a more clear and precise terminology, the glossectomy classification by Ansarin et al. facilitates shared communication between surgeons, allowing comparison between published research and improving surgical practice and patient care. To establish the association of glossectomies, according to their classification by Ansarin et al. with overall survival (OS), disease-free survival (DSF), and cause-specific survival (CSS) in tongue cancer, we conducted a systemic retrospective study on 300 consecutive patients affected by primary oral tongue cancer and treated with surgery at the European Institute of Oncology, IRCCS (IEO).MethodsThree hundred patients with tongue squamous cell carcinoma and treated at the Division of Otorhinolaryngology and Head and Neck Surgery of the European Institute of Oncology, IRCCS were cataloged according to the glossectomy classification. OS, DFS, and CSS were compared by surgical treatments.ResultsOS-5yrs was 80% for the type I glossectomy group, 75% for type II, 65% for type III, and 35% for type IV-V. DFS-5yrs was 74%, 60%, 55%, and 27%, respectively for I, II, III, and IV-V glossectomy group; CSS-5yrs was 82%, 80%, 72%, and 48%, respectively for I, II, III, and IV-V glossectomy group (p < 0.01).ConclusionsThis study confirmed that the application of the glossectomy classification was statistically correlated with patients' oncological outcomes.     

Locally advanced cervical carcinoma patients treated with chemoradiation followed by radical surgery: clinical response and oncological outcomes according to histotype after propensity score analysis

IntroductionThe aims of this study were to analyze the pathological response, and survival outcomes of adenocarcinoma/adenosquamous (AC/ASC) versus squamous cell carcinoma (SCC) in patients with locally advanced cervical cancer (LACC) managed by chemoradiotherapy followed by radical surgery.MethodsRetrospective, multicenter, observational study, including patients with SCC and AC/ACS LACC patients treated with preoperative CT/RT followed by tailored radical surgery (RS) between 06/2002 and 05/2017. Clinical-pathological characteristics were compared between patients with SCC versus AC/ASC. A 1:3 ratio propensity score (PS) matching was applied to remove the variables imbalance between the two groups.ResultsAfter PS, 320 patients were included, of which 240 (75.0%) in the SCC group, and 80 (25.0%) in the AC/ASC group. Clinico-pathological and surgical baseline characteristics were balanced between the two study groups. Percentage of pathologic complete response was 47.5% in SCC patients versus 22.4% of AC/ASC ones (p < 0.001). With a median follow-up of 51 months (range:1–199), there were 54/240 (22.5%) recurrences in SCC versus 28/80 (35.0%) in AC/ASC patients (p = 0.027). AC/ASC patients experienced worse disease free (DFS), and overall survival (OS) compared to SCC patients (p = 0.019, and p = 0.048, respectively). In multivariate analysis, AC/ACS histotype, and FIGO stage were associated with worse DFS and OS.ConclusionIn LACC patients treated with CT/RT followed by RS, AC/ASC histology was associated with lower pathological complete response to CT/RT, and higher risk of recurrence and death compared with SCC patients. This highlights the need for specific therapeutic strategies based on molecular characterization to identify targets and develop novel treatments.     

Odontogenic cysts: classification,histological features and a practical approach to common diagnostic problems

The epithelial remnants of tooth development give rise to an impressive range of cystic lesions, termed odontogenic cysts. They are classified based on their distinct clinical, radiological and histological features, a process that has not been without controversy. We will attempt to explain the basis of the debate behind the changing classification of odontogenic cysts, describing their aetiology, clinical and histological features, along with common pitfalls that can confuse the diagnostic process. More common diagnostic challenges, such as the effects of inflammation and mucous change, will be explored in detail. An attempt will be made to distil the diagnostic process into simple algorithmic steps to narrow down the differential diagnoses of this fascinating group of lesions. We will demonstrate the importance of careful consideration of the clinical and radiological features that can help prevent misclassification, ensuring appropriate management and follow-up for this diverse group of lesions.     

慢性心力衰竭护理结局分类评价表的构建研究

目的本研究以护理结局分类(NOC)为基础,筛选符合慢性心力衰竭患者的相关护理结局,由此构建具有专科专病特点的慢性心力衰竭患者护理结局评价表。方法成立研究核心小组,应用文献回顾法、专家函询法确定护理结局分类指标,并进行统计学分析,最终建立慢性心力衰竭护理结局分类评价表。结果通过研究,确定慢性心力衰竭护理结局中包含4个领域、8个一级指标、68个二级指标。4个领域分别是生理健康、功能健康、健康知识和行为、心理社会健康。一级指标中包含心脏泵血功能、活动耐力、社会支持、抑郁水平等。二级指标中包含心脏指数、血压、呼吸困难、情绪波动大、步行距离、步行速度等68个指标。统计学分析中:第一、二轮函询专家的积极系数分别为90%、100%,专家权威系数为0.895,专家意见协调程度(P<0.05)。结论本研究成功建立慢性心力衰竭护理结局分类评价表,有助于临床护士用于评价护理措施的有效性,也使其成为一种标准化语言,便于应用到护理电子信息系统中进行绩效测算等。但是本研究不足之处是未能将该研究进行临床实证,未能进一步确认其有效性。