To construct an intelligent assistant diagnosis model based on the clinical images of basal cell carcinoma (BCC) and pigmented nevi in Chinese by using the advanced convolutional neural network (CNN).
 Methods: Based on the Xiangya Medical Big Data Platform, we constructed a large-scale clinical image dataset of skin diseases according to Chinese ethnicity and the Xiangya Skin Disease Dataset. We evaluated the performance of 5 mainstream CNN models (ResNet50, InceptionV3, InceptionResNetV2, DenseNet121, and Xception) on a subset of BCC and pigmented nevi of this dataset. We also analyzed the basis of the diagnosis results in the form of heatmaps. We compared the optimal CNN classification model with 30 professional dermatologists.
 Results: The Xiangya Skin Disease Dataset contains 150 223 clinical images with lesion annotations, covering 543 skin diseases, and each image in the dataset contains support for pathological gold standards and the patient's overall medical history. On the test set of 349 BCC and 497 pigmented nevi, the optimal CNN model was Xception, and its classification accuracy can reach 93.5%, of which the area under curve (AUC) values were 0.974 and 0.969, respectively. The results of the heatmap showed that the CNN model can indeed learn the characteristics associated with disease identification. The ability of the Xception model to identify clinical images of BCC and Nevi was basically comparable to that of professional dermatologists.
 Conclusion: This study is the first assistant diagnosis study for skin tumor based on Chinese ethnic clinical dataset. It proves that CNN model has the ability to distinguish between Chinese ethnicity's BCC and Nevi, and lays a solid foundation for the following application of artificial intelligence in the diagnosis and treatment for skin tumors.
Area Under Curve ; Carcinoma, Basal Cell ; diagnostic imaging ; Humans ; Neural Networks, Computer ; Nevus, Pigmented ; diagnostic imaging ; Skin Neoplasms ; diagnostic imaging

OBJECTIVES: Accurate breast lesion surface localization can guarantee accurate biopsy and local treatment. But there is no guideline to regular equipment and methods for the localization of breast lesions. The conventional non-invasive localization method is marker-based localization. The advantages of this method are simple and efficient. The disadvantages are that markers disappear easily under coupling agents; the positioning length of markers cannot last long on skin; and healthcare associated infection due to many patients using the same marker pen is potentially unavoidable. Breast lesion sticker (called sticker for short) is a new-type localization medical instrument in 2020. Our study aims to explore the clinical value of a new lesion stickers in breast lesion surface localization via comparison of the sticker and marker pen localization methods. METHODS: This was a prospective cohort study. It was conducted in 67 patients who needed breast lesion surface localization before biopsy. The patients were randomly assigned into 2 groups. One group of patients used marker pen to mark breast lesion surface location by ultrasonography. The other group of patients used stickers. Patients labeled with markers on skin were swabbed agents before marking. Then the markers were checked by ultrasound scan. If the surface positions of breast lesion were not correct, the above procedure was repeated. In the sticker group, the stickers were released synchronously after the lesions were detected by ultrasound scan. Then locations were checked via scanning hole. If the surface positions of breast lesion were not correct, the above procedure was repeated. The accuracy of positioning, the length of positioning time and satisfaction of patients between the 2 groups were compared. The length of positioning time was calculated from the time when ultrasound detected the lesion to the time when the surface position of breast lesion was confirmed. The total score of patients' satisfaction was 5 points according to Service Quality Evaluation of SERVQUAL Scale, including sonographers' service attitude and their technical proficiency, other medical staffs' service attitude and their technical proficiency, hospital service procedures, positioning comfort, and positioning effects. RESULTS: All 67 patients were females, aged 18-66 (39.73±13.10). There were 35 patients in the marker pen group and 32 patients in the sticker group. The time length of group used marker pen to localization was 22-88 (52.20±2.90) s, and the sticker group was 3-15 (9.22±0.58) s in length. The length of positioning time for the stickers was significantly shorter than that of the marker (P<0.01). Both methods were accurate in the surface localization of lesions before operation. The total scores of patients' satisfaction was 4-5 (4.92±0.02) in the stickers group, and 1-5 (3.35±0.10) in the marker pen group. The patients' satisfaction scores with the sticker were significantly higher than those with the marker pen (P<0.01). The length of positioning time and patients' satisfication scores for sonographer with 20 years' working experience were shorter and higher than those of sonographer with 10 years' working experience, respectively (both P<0.05). CONCLUSIONS The new breast lesion positioning stickers have more advantages than the marker pen in localization efficiency. It could reduce the workload of medical workers and increase patients' satisfaction to some extent. The stickers can be used not only in the breast lesions surface localization, but also in the skin location of pleural effusion and ascites, the skin location of surface masses, the skin location of thyroid nodule, and many other clinical marker areas, to further expand the scope of clinical application and value of the stickers.
Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Female ; Humans ; Male ; Prospective Studies ; Skin

Aged ; Carcinoma, Hepatocellular ; complications ; diagnosis ; diagnostic imaging ; Fatal Outcome ; Humans ; Liver Neoplasms ; complications ; diagnosis ; diagnostic imaging ; Male ; Skin Neoplasms ; diagnosis ; diagnostic imaging ; secondary

Acute Kidney Injury ; diagnostic imaging ; etiology ; Aged ; Humans ; Male ; Necrosis ; diagnostic imaging ; etiology ; Paraganglioma ; complications ; Postoperative Complications ; Retroperitoneal Neoplasms ; complications ; Skin ; diagnostic imaging ; pathology ; Tomography, X-Ray Computed

OBJECTIVETo investigate the clinical efficiency of electrocoagulation for the treatment of noninvoluting congenital hemangioma.

METHODSSixteen infants with noninvoluting congenital hemangioma who were admitted to our hospital from January 2011 to June 2013 were included in this study. Color Doppler ultrasound was used to determine the hemangioma location, as well as its size and depth. High frequency electrocoagulation was adopted for the treatment. The output power was set at 10-20 W. The probes were inserted around the tumor or at the surface of the tumor. After switching on for 1-2 seconds, the direction and position of the probe was modulated until covering the whole tumor. After the treatment, the absorption of tumor was about 3-6 months. The efficiency was evaluated during the follow-up.

RESULTSTumor atrophy was obvious after treatment in all patients. The temperature around the tumor mass was decreased, and the aberrant blood signals were decreased under the ultrasonic examination. Complete or partial atrophy were observed. The efficiency was graded as level I, II, III, IV in 0, 2, 9 and 5 patients, respectively. One patient showed local infection due to improper nursing, which was completely relieved after corresponding treatment. No severe adverse events were observed.

CONCLUSIONSHigh-frequency electrocoagulation is effective for treating noninvoluting congenital hemangioma through coagulating the aberrant blood vessels in the tumor, interrupting the vascular endothelial cell, blocking the aberrant blood flow, as well as leading to atrophy and absorption of tumor mass. Besides, no obvious scar is observed after the surgery.

Electrocoagulation ; methods ; Hemangioma ; congenital ; diagnostic imaging ; surgery ; Hemangioma, Capillary ; congenital ; diagnostic imaging ; surgery ; Humans ; Infant ; Skin Neoplasms ; congenital ; diagnostic imaging ; surgery ; Temperature ; Ultrasonography

In recent years, deep learning has been used in many researches in cancer screening based on medical imaging. Among cancer screening using optical imaging, melanoma detection is the biggest concern. Stanford University researchers used CNNs (convolutional neural networks) to classify skin lesions comparing with 21 dermatologists for 2 tasks. CNN performed better than all the dermatologists' tasks. Finding pulmonary nodules on chest X-ray has the longest history in cancer screening using medical imaging and neural network technology began to be applied before the deep learning technology matured as it is now. But, the applications were mainly focused on screening in CT images. There is relatively few research on pulmonary nodule detection using deep learning in chest X-rays. For breast cancer screening in mammography, adoption of neural network technologies has already begun early. Many studies have shown that tumor detection using CNNs is useful in breast cancer screening. Most of the results are from mammography, but studies using tomosynthesis, ultrasound, and MRI have also been published. Although imaging modality and target cancer are different, we can see that there are similar kinds of future challenges. First, it is not easy to acquire a large amount of medical image data required for deep learning. Second, it is difficult to learn if there are many medical image data but they are not properly labeled. Finally, there is a need for technologies that can use different imaging modalities at the same time, link with electronic health records, and use genetic information for more comprehensive screening.
Breast Neoplasms ; Diagnostic Imaging* ; Early Detection of Cancer* ; Electronic Health Records ; Learning* ; Magnetic Resonance Imaging ; Mammography ; Mass Screening ; Melanoma ; Optical Imaging ; Skin ; Skin Neoplasms ; Thorax ; Ultrasonography

Aged ; Diagnosis, Differential ; Fluorodeoxyglucose F18 ; Gout ; diagnostic imaging ; Humans ; Joints ; diagnostic imaging ; Male ; Melanoma ; diagnostic imaging ; secondary ; Multimodal Imaging ; Positron-Emission Tomography ; Radiopharmaceuticals ; Skin Neoplasms ; diagnostic imaging ; secondary ; Tomography, X-Ray Computed

It is well known that the medullary carcinoma of the breast is one of the special types of breast carcinoma with a good prognosis. At present, the medullary carcinoma of the breast is subclassified into 3 types: typical medullary, atypical medullary and nonmedullary carcinoma. Among them, the former has the best prognosis. We reviewed the film mammographic and ultrasonomammographic findings of 13 patients according to the reevaluated histopathologic diagnosis. Typical medullary carcinoma shows a well circumscribed mass with surrounding halo on film mammogram, and well defined mass with central intermediate echogenicity and peripheral low echogenicity and posterior acoustic enhancement on ultrasonomammogram. Atypical medullary carcinoma shows relatively well circumscribed mass with partial marginal obliteration on film mammogram, and irregular bordered mass with inhomogeneous echogenicity due to focal necrosis in the mass and associated findings of thick boundary, asymetrical lateral shadowing on ultrasonomammogram. Nonmedullary carcinoma shows lobulated mass with surrounding parenchymal distortion and skin thickening on film mammogram, and relatively well defined lobulating mass with surrounding parenchymal distortion and marked heterogeneous internal echogenicity on ultrasonomammogram. Therefore, differentiation between typical medullary carcinoma with good prognosis and atypical medulary or nonmedullary carcinoma with poor prognosis, may be possible by various diagnostic imaging modalities preoperatively. But further collective study shall be needed in near future.
Acoustics ; Breast Neoplasms ; Breast* ; Carcinoma, Medullary* ; Classification* ; Diagnosis ; Diagnostic Imaging ; Humans ; Necrosis ; Prognosis ; Shadowing (Histology) ; Skin

Dermoscopy ; Diagnosis, Differential ; Face ; Humans ; Nevus, Pigmented ; Skin Neoplasms/diagnostic imaging* ; Wood

Dermoscopy ; Diagnosis, Differential ; Humans ; Microscopy, Confocal ; Psoriasis/diagnostic imaging* ; Skin Neoplasms/diagnosis*