Necrotizing sialometaplasia: a malignant masquerade but questionable precancerous lesion, report of four cases

Abstract Background Necrotizing sialometaplasia (NSM) is an extremely rare benign lesion with an uncertain pathogenesis. The differential diagnosis of this lesion is challenging due to little familiarity with this entity and histologic similarity with carcinomas, especially mucoepidermoid carcinoma (MEC). The purpose of this study is to raise awareness about NSM, which is often overlooked or misdiagnosed as malignancy in a small biopsy. Methods We reviewed all biopsy materials taken from the oral cavity in a single institution in Korea from 2012 to 2018 and found 4 cases of NSM out of 726. Clinicopathologic characteristics and comparison with other lesions were discussed. Results Unlike previous reports, patients in our series were relatively young, and NSM was not related to smoking and not associated with malignancies, although one patient was misdiagnosed with MEC on the basis of the initial biopsy. High-grade squamous dysplasia was observed in one patient; however, all four patients showed excellent prognoses without further management. Conclusions A conservative approach is recommendable for necrotizing lesions of the palate in young adults to avoid unnecessary treatment. However, careful monitoring is also required due to uncertainty of premalignant potential

Solitary Primary Gastric Mantle Cell Lymphoma

Mantle cell lymphoma (MCL) is a relatively rare subgroup of non-Hodgkin's lymphoma that is characterized by an aggressive and severe disease course with frequent involvement of regional lymph nodes and/or early metastasis. Because most cases of MCL are diagnosed in the advanced stages, clinical data on extranodal or early stage MCL is lacking, and MCL that is both extranodal and diagnosed during the early stages is even more rare. There have been several case reports on primary gastric MCL, which comprise a type of extranodal MCLs. However, to our knowledge, there have been no reports on solitary primary gastric MCL without regional lymph node involvement or distant metastasis. Recently, the authors experienced an uncommon case of MCL with the aforementioned characteristics that was managed with chemotherapy followed by allogenic stem cell transplantation

Favorable Vasomotor Function after Drug-Coated Balloon-Only Angioplasty of De Novo Native Coronary Artery Lesions

Balloon-injured coronary segments are known to harbor abnormal vasomotion. We evaluated whether de novo coronary lesions treated using drug-coated balloon (DCB) are prone to vasospasm and how they respond to ergonovine and nitrate. Among 132 DCB angioplasty recipients followed, 89 patients underwent ergonovine provocation test at 6–9 months follow-up. Within-subject ergonovine- and nitrate-induced diameter changes were compared among three different sites: DCB-treated vs. angiographically normal vs. segment showing prominent vasoreactivity (spastic). No patient experienced clinically refractory vasospastic angina or symptom-driven revascularization during follow-up. Ergonovine induced vasospasm in seven patients; all were multifocal spasm either involving (n = 2) or rather sparing DCB-treated segments (n = 5). None showed focal spasm that exclusively involved DCB-treated lesions. Among 27 patients with vasospastic features, DCB-treated segments showed less vasoconstriction than spastic counterparts (p < 0.001). A total of 110 DCB-treated lesions were analyzed to assess vasomotor function. Vasomotor function, defined as a combined constrictor and dilator response, was comparable between DCB-treated and angiographically normal segments (p = 0.173), while significant differences were observed against spastic counterparts (p < 0.001). In our study, DCB-treated lesions were not particularly vulnerable to vasospasm and were found to have vasomotor function similar to angiographically normal segments, supporting safety of DCB-only strategy in treating de novo native coronary lesions

Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67