Diabetic foot ulcers: A devastating complication of diabetes mellitus continues non-stop in spite of new medical treatment modalities

Diabetic foot ulcer is a devastating complication of diabetes mellitus and significant cause of mortality and morbidity all over the world and can be complex and costly. The development of foot ulcer in a diabetic patient has been estimated to be 19%-34% through their lifetime. The pathophysiology of diabetic foot ulcer consist of neuropathy, trauma and, in many patients, additional peripheral arterial disease. In particular, diabetic neuropathy leads to foot deformity, callus formation, and insensitivity to trauma or pressure. The standard algorithms in diabetic foot ulcer management include assessing the ulcer grade classification, surgical debridement, dressing to facilitate wound healing, off-loading, vascular assessment (status and presence of a chance for interventional vascular correction), and infection and glycemic control. Although especially surgical procedures are sometimes inevitable, they are poor predictive factors for the prognosis of diabetic foot ulcer. Different novel treatment modalities such as nonsurgical debridement agents, oxygen therapies, and negative pressure wound therapy, topical drugs, cellular bioproducts, human growth factors, energy-based therapies, and systematic therapies have been available for patients with diabetic foot ulcer. However, it is uncertain whether they are effective in terms of promoting wound healing related with a limited number of randomized controlled trials. This review aims at evaluating diabetic foot ulcer with regard to all aspects. We will also focus on conventional and novel adjunctive therapy in diabetic foot management.     

Automatic segmentation of thoracic CT images using three deep learning models

PurposeDeep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. Image segmentation using DL has given very promising results. In this work, we present and compare the results of segmentation of OARs and a clinical target volume (CTV) in thoracic CT images using three DL models.Materials and methodsWe used CT images of 52 patients with breast cancer from a public dataset. Automatic segmentation of the lungs, the heart and a CTV was performed using three models based on the U-Net architecture. Three metrics were used to quantify and compare the segmentation results obtained with these models: the Dice similarity coefficient (DSC), the Jaccard coefficient (J) and the Hausdorff distance (HD).ResultsThe obtained values of DSC, J and HD were presented for each segmented organ and for the three models. Examples of automatic segmentation were presented and compared to the corresponding ground truth delineations. Our values were also compared to recent results obtained by other authors.ConclusionThe performance of three DL models was evaluated for the delineation of the lungs, the heart and a CTV. This study showed clearly that these 2D models based on the U-Net architecture can be used to delineate organs in CT images with a good performance compared to other models. Generally, the three models present similar performances. Using a dataset with more CT images, the three models should give better results.     

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.     

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.     

An update on the classification of phyllodes tumours of the breast

Phyllodes tumour (PT) is a rare fibroepithelial neoplasm of the breast, histologically categorized into 3 grades, namely benign, borderline and malignant; each with distinct clinical behaviour and implications for management. This article addresses challenges in the diagnosis, grading and management of PT in excisional and core needle biopsy specimens, with a brief review of recent molecular findings.     

Different pulmonary adenocarcinoma growth patterns significantly affect survival

ObjectiveAdenocarcinoma (AC) is the number one pathological entity of lung cancer with approximately 30–40% of cases. It is known to be heterogeneous and has 5 histopathological growth patterns. We evaluated the long-term survival rates of patients with predominant subtypes.Methods290 patients with AC underwent pulmonary resection between 2012 and 2017 at our institution. We excluded all patients with lymph node involvement and distant metastases. Hence, 163 patients were included for further analysis. Predominant growth pattern was defined if more than 10% of cells showed a growth pattern. 1, 3, and 5-year survival rates were evaluated. Survival was assessed by Kaplan-Meier curves and the Cox proportional hazards model was used to identify prognostic factors for overall survival.ResultsPredominant growth patterns >10% were compared to <10% growth patterns of the same subtype. 1-year, 3-year, and 5-year overall survival rates of patients with predominant solid tumor growth >10% differed significantly from patients with <10% (88.4% vs. 97.6%, p = 0.04; 65.8% vs. 87.4% p = 0.001, 36.4% vs. 65.9% p = 0.01). Survival rates did not differ between >10% papillary and acinar growth compared to <10%. Kaplan-Meier curves showed reduced overall survival for patients with solid tumor growth >10% (log-rank 0.002). Solid tumor growth >10% was an independent prognostic factor for worse long-term survival (Hazard ratio: 3.05, p = 0.01).ConclusionOur study demonstrates that the presence of a predominant solid pattern in pulmonary adenocarcinoma is a factor for an unfavorable prognosis. This should be kept in mind in daily clinical practice.     

应用锥形束CT对种植区牙槽骨质量分类方法的改良研究

目的    应用锥形束CT（cone beam CT，CBCT）测量种植区牙槽骨质量并结合Lekholm和Zarb分类法对骨质量分类方法进行改良，以期为提高牙槽骨质量分类的准确性提供指导。方法    选择2015年10月至2017年10月在呼和浩特市口腔医院种植科进行种植修复的196例患者术前CBCT影像资料，应用 Invivo5诊断设计软件测量306个种植位点的牙槽骨皮质骨厚度和松质骨密度（皮质骨与松质骨的CT值差值），以Lekholm和Zarb分类法为基础结合测量数据对骨质量进行改良后的量化分类。结果    牙槽骨皮质骨厚度为0.18 ~ 2.89 mm，中位数为0.92 mm。皮质骨与松质骨的CT值差值为88.5 ~ 667.8 HU，应用百分位数法找到33.3%和66.6%对应的数值分别为297.8 HU和356.1 HU。依据此数据结合Lekholm和Zarb分类法将196例患者的306个种植位点骨质量分为4类，其中Ⅰ类骨种植位点46个（占15.0%），Ⅱ类骨104个（占34.0%），Ⅲ类骨114个（占37.3%），Ⅳ类骨42个（占13.7%）。结论    结合Lekholm和Zarb分类法，应用CBCT测量种植区牙槽骨质量并进行分类方法的改良，可为临床医生术前评估牙槽骨质量提供一定参考。