Objective:To explore the imaging features of rare tumors of nasal cavity and sinuses, and to improve the understanding of these diseases, thereby aiding clinical diagnosis and treatment. Methods:The CT and MRI findings of 79 cases of rare neoplasm of nasal cavity and sinuses confirmed by pathology were retrospectively analyzed, and the imaging features were summarized. Results:Among the 79 cases, there were 16 cases of neuroendocrine carcinoma, most showing expansive and infiltrative bone destruction without hyperosteogeny and sclerosis. The sphenoid sinus exhibited a "pigeon" shape. In 28 cases of malignant melanoma, MRI signals were diverse, typical signals were rare, but mixed signals were more common. In 12 cases of rhabdomyosarcoma, MRI enhancement mostly showed "grape-like" enhancement and partial ring enhancement; There were 10 cases of olfactory neuroblastoma, the lesions were consistent with the distribution area of olfactory mucosa, most of them were lobulated, marginal nodules, and "flower ring" enhancement, and 2 cases grew across intracranial and external, with multiple cystic lesions and surrounding flaky edema bands. In 5 cases of solitary fibrous tumor, Benign tumors had regular shape and uniform density, while malignant tumors had irregular shape and uneven density, The enhancement was obviously uneven and showed a "pattern" change. There were 2 cases of sarcomatoid carcinoma, both with lobed appearance, uneven density, lamellar low-density shadow, and osteolytic bone destruction. In 4 cases of schwannoma, the enhancement showed obvious inhomogeneous enhancement. One case showed cystic necrosis, one case showed calcification, and the surrounding structure was compressed without damage. There was 1 case of neurofibroma, with many cystic components, low signal separation and compartmentalized enhancement. One case of paraganglioma showed moderate enhancement in the arterial phase and progressive enhancement in the venous phase, accompanied by significant swelling bone destruction. Conclusion:Rare tumors of nasal cavity and paranasal sinuses have distinctive imaging features. CT and MRI can effectively show the extent of the lesions and the degree of infiltration into adjacent tissues and organs, which is helpful for early clinical diagnosis and staging. However, definitive diagnosis still depends on pathology and immunohistochemistry.

Objective:To explore the accuracy of artificial intelligence (AI) system based on deep learning in evaluating bone age of children with abnormal growth and development.Methods:The positive X-ray films of the left wrist of children with abnormal growth and development who were treated at the Affiliated Hospital of Guizhou Medical University from January 2020 to December 2021 were collected retrospectively. A total of 717 children were collected, including 266 males and 451 females, aged 2-18 (11±3) years. Based on Tanner Whitehouse 3 (TW 3)-RUS (radius, ulna, short bone) and TW3-Carpal (carpal bone) method, bone age was measured by 3 senior radiologists, and the mean value was taken as reference standard. The bone ages were independently evaluated by the AI system (Dr.Wise bone age prediction software) and two junior radiologists (physicians 1 and 2). The accuracy within 0.5 year, the accuracy within 1 year, the mean absolute error (MAE) and the root mean square error (RMSE) between the evaluation results and the reference standard were analyzed. Paired sample t-test was used to compare MAE between AI system and junior physicians. Intraclass correlation coefficient (ICC) was used to evaluate the consistency between AI system, junior physician and reference standard. The Bland-Altman diagram was drawn and the 95% consistency limit was calculated between AI system and reference standard. Results:For TW3-RUS bone age, compared with the reference standard, the accuracy within 0.5 year of AI system, physician 1 and physician 2 was 75.3% (540/717), 62.1% (445/717) and 66.2% (475/717), respectively. The accuracy within 1 year was 96.9% (695/717), 86.3% (619/717) and 89.1% (639/717), respectively. MAE was 0.360, 0.565 and 0.496 years, and RMSE was 0.469, 0.634 and 0.572 years, respectively. For TW3-Carpal bone age, compared with the reference standard, the accuracy within 0.5 year of AI system, physician 1 and physician 2 was 80.9% (580/717), 65.1% (467/717) and 71.7% (514/717), respectively. The accuracy within 1 year was 96.0% (688/717), 87.3% (626/717) and 90.4% (648/717), respectively. MAE was 0.330, 0.527 and 0.455 years, and RMSE was 0.458, 0.612, 0.538 years, respectively. Based on TW3-RUS and TW3-Carpal bone age, the MAE of AI system were lower than those of physician 1 and physician 2, and the differences were statistically significant ( P all<0.001). The evaluation results of AI, physician 1 and physician 2 were in good agreement with the reference standard (ICC all>0.950). The Bland-Altman analysis showed that the 95% agreement limits of AI system for assessing TW3-RUS and TW3-Carpal bone age were -0.75-1.02 years and-0.86-0.91 years, respectively. Conclusion:The accuracy of AI system in evaluating the bone age of children with abnormal growth and development is close to that of senior doctors, better than that of junior doctors, and in good agreement with senior doctors.

This study aimed to investigate whether magnetic resonance imaging (MRI) features could differentiate non-hypervascular pancreatic neuroendocrine tumors (PNETs) from pancreatic ductal adenocarcinomas (PDACs). In this study, 131 patients with surgically and pathologically proven non-hypervascular PNETs (n = 44) or PDACs (n = 87) were enrolled. Two radiologists independently analyzed MRI imaging findings and clinical features. Relevant features in differentiating non-hypervascular PNETs from PDACs were identified via univariate and multivariate logistic regression models. The MRI feature-based nomogram was constructed based on multivariable logistic analysis and the reliability of the constructed nomogram was further validated. The results showed that tumor margin (P = 0.012; OR: 6.622; 95% CI: 1.510, 29.028), MPD dilation (P = 0.047; OR: 4.309; 95% CI: 1.019, 18.227), and signal in the portal phase (P < 0.001; OR: 53.486; 95% CI: 10.690, 267.618) were independent discriminative MRI features between non-hypervascular PNETs and PDACs. The discriminative performance of the developed nomogram was optimized compared with single imaging features. The calibration curve, C-index, and DCA validated the superior practicality and usefulness of the MRI-based nomogram. In conclusion, the radiologically discriminative model integrating various MRI features could be preoperatively and easily utilized to differentiate non-hypervascular PNETs from PDACs.

病例 病例1,女,76岁,因排便不畅2月多,扪及左下腹肿块1月多入院.专科检查:阴道内可见少量分泌物,轻微宫颈抬举痛.实验室检查:CA125 54.67 U/mL.妇科超声:子宫前壁至脐下一实性低回声团块,形态欠规则,边界尚清,大小约148 mm×125 mm×126 mm,内部回声较均匀,后方无声影(图1),CDFI:可见散在血流信号(图2).超声诊断:子宫前壁平滑肌瘤,富于细胞型.CT平扫+增强:盆腔见一团块状囊实性灶(图3),大小约150mm×140mm×190mm,增强扫描未见明显强化,其内可见分隔,局部壁稍显增厚,呈轻度强化,局部病灶内可见结节状高密度钙化影(图4),邻近肠壁、膀胱及子宫稍受压,病灶与左侧附件区关系密切.影像诊断:盆腔左侧囊实性占位,恶性可能大,倾向来源于附件.