Notes

Beyond limits? The perimeter specs dilemma regarding centrality inside emotional sites.
The Caprini risk assessment model (RAM) and D-dimer testing have been widely used in the prediction of venous thromboembolism (VTE). However, the clinical significance of these testing options are limited in non-oncological urological inpatients because of the low specificity.

This retrospective study included 1,453 patients who were admitted to the non-oncological unit of the Department of Urology, Xiangya Hospital, Central South University, from January 2018 to December 2018. The highest score of Caprini RAM and the highest D-dimer level were collected in this retrospective study. Ultrasound examinations of the lower extremities or computed tomographic pulmonary arteriography (CTPA) were applied to patients who were suspected of having VTE, if necessary.

A total of 1,453 patients were collected in this study, which included 34 VTE and 1,419 non-VTE patients. The threshold of D-dimer was 0.89 µg/mL, according to the receiver operating characteristic (ROC) curve, with a sensitivity of 82.4%, a specificints.[This corrects the article DOI 10.21037/tlcr-20-177.].Patients with lung cancer in the majority die of metastases. Treatment options include surgery, chemo- and radiotherapy, targeted therapy by tyrosine kinase inhibitors (TKIs), and immuno-oncologic treatment. Despite the success with these treatment options, cure of lung cancer is achieved in only a very small proportion of patients. In most patients' recurrence and metastasis will occur, and finally kill the patient. Metastasis is a multistep procedure. It requires a change in adhesion of tumor cells for detachment from their neighboring cells. The next step is migration either as single cells [epithelial-mesenchymal transition (EMT)], or as cell clusters (hybrid-EMT or bulk migration). A combination of genetic changes is required to facilitate migration. Then tumor cells have to orient themselves along matrix proteins, detect oxygen concentrations, prevent attacks by immune cells, and induce a tumor-friendly switch of stroma cells (macrophages, myofibroblasts, etc.). Having entered the blood stream tumor cel requisite, but does not necessarily predict metastasis. The intention of this review is to point to these different aspects and hopefully provoke research directed into a more functional analysis of the metastatic process.The emergence of whole slide imaging technology allows for pathology diagnosis on a computer screen. The applications of digital pathology are expanding, from supporting remote institutes suffering from a shortage of pathologists to routine use in daily diagnosis including that of lung cancer. see more Through practice and research large archival databases of digital pathology images have been developed that will facilitate the development of artificial intelligence (AI) methods for image analysis. Currently, several AI applications have been reported in the field of lung cancer; these include the segmentation of carcinoma foci, detection of lymph node metastasis, counting of tumor cells, and prediction of gene mutations. Although the integration of AI algorithms into clinical practice remains a significant challenge, we have implemented tumor cell count for genetic analysis, a helpful application for routine use. Our experience suggests that pathologists often overestimate the contents of tumor cells, and the use of AI-based analysis increases the accuracy and makes the tasks less tedious. However, there are several difficulties encountered in the practical use of AI in clinical diagnosis. These include the lack of sufficient annotated data for the development and validation of AI systems, the explainability of black box AI models, such as those based on deep learning that offer the most promising performance, and the difficulty in defining the ground truth data for training and validation owing to inherent ambiguity in most applications. All of these together present significant challenges in the development and clinical translation of AI methods in the practice of pathology. Additional research on these problems will help in resolving the barriers to the clinical use of AI. Helping pathologists in developing knowledge of the working and limitations of AI will benefit the use of AI in both diagnostics and research.The use of molecular diagnostics in the diagnosis and management of patients with advanced lung cancer has become widespread. Although molecular classification has increasingly been incorporated in the pathologic classification of certain types of human tumors (particularly within the hematologic, glial, and bone/soft tissue malignancies), genetic findings have not been formally incorporated into the pathologic classification of lung cancer, which presently relies solely on the assessment of histologic and immunophenotypic characteristics. Whether molecular classification should be adopted in lung cancer would depend on the diagnostic, prognostic, and predictive impacts of such classification-and whether these impacts confer significant values additive to those derived from the routine histologic and immunophenotypic assessment. We provide a brief overview on the genetics of lung cancer, including adenocarcinoma, squamous cell carcinoma, and neuroendocrine tumors (small cell carcinoma, large cell neuroendocrine carcinoma, and carcinoid tumors). We consider the values of molecular information with some examples, in terms of the current diagnostic, prognostic, and predictive impacts. Finally, we discuss the conceptual and technical challenges of adopting a molecular classification for lung cancer in clinical management for patients. While there are conceptual and technical hurdles to tackle in implementing molecular classification in the pathologic classification of lung cancer, such integrated histologic-molecular diagnosis may allow one to personalize and optimize therapy for patients with advanced lung cancer.Large cell neuroendocrine carcinoma (LCNECs) and small cell lung carcinomas (SCLCs) are high-grade neuroendocrine carcinomas of the lung with very aggressive behavior and poor prognosis. Their histological classification as well as their therapeutic management has not changed much in recent years, but genomic and transcriptomic analyses have revealed different molecular subtypes raising hopes for more personalized treatment. Indeed, four subtypes of SCLCs have been recently described, SCLC-A driven by the master gene ASCL1, SCLC-N driven by NEUROD1, SCLC-Y by YAP1 and SCLC-P by POU2F3. Whereas SCLC standard of care is based on concurrent chemoradiation for limited stages and on chemotherapy alone or chemotherapy combined with anti-PD-L1 checkpoint inhibitors for extensive stage SCLC, SCLC-A variants could benefit from DLL3 or BCL2 inhibitors, and SCLC-N variants from Aurora kinase inhibitors combined with chemotherapy, or PI3K/mTOR or HSP90 inhibitors. In addition, a new SCLC variant (SCLC-IM) with high-expression of immune checkpoints has been also reported, which could benefit from immunotherapies.
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