Objective To analyze the radiomic and clinical features extracted from 2D ultrasound images of thyroid tumors in patients with Hashimoto's thyroiditis(HT)combined with papillary thyroid carcinoma(PTC)using machine learning(ML)models,and to explore the diagnostic performance of the method in making preoperative noninvasive identification of cervical lymph node metastasis(LNM).Methods A total of 528 patients with HT combined with PTC were enrolled and divided into two groups based on their pathological results of the presence or absence of LNM.The groups were subsequently designated the With LNM Group and the Without LNM Group.Three ultrasound doctors independently delineated the regions of interest and extracted radiomic features.Two modes,radiomic features and radiomics-clinical features,were used to construct random forest(RF),support vector machine(SVM),LightGBM,K-nearest neighbor(KNN),and XGBoost models.The performance of these five ML models in the two modes was evaluated by the receiver operating characteristic(ROC)curves on the test dataset,and SHapley Additive exPlanations(SHAP)was used for model visualization.Results All five ML models showed good performance,with area under the ROC curve(AUC)ranging from 0.798 to 0.921.LightGBM and XGBoost demonstrated the best performance,outperforming the other models(P＜0.05).The ML models constructed with radiomics-clinical features performed better than those constructed using only radiomic features(P＜0.05).The SHAP visualization of the best-performing models indicated that the anteroposterior diameter,superoinferior diameter,original_shape_VoxelVolume,age,wavelet-LHL_firstorder_10Percentile,and left-to-right diameter had the most significant effect on the LightGBM model.On the other hand,the superoinferior diameter,anteroposterior diameter,left-to-right diameter,original_shape_VoxelVolume,original_firstorder_InterquartileRange,and age had the most significant effect on the XGBoost model.Conclusion ML models based on radiomics and clinical features can accurately evaluate the cervical lymph node status in patients with HT combined with PTC.Among the 5 ML models,LightGBM and XGBoost demonstrate the best evaluation performance.

Pyogenic arthritis, pyoderma gangrenosum and acne (PAPA) syndrome is a rare autoinflammatory bone disease, which caused by proline-serine-threonine phosphatase interacting protein 1 (PSTPIP1) gene mutations.Due to a lack of awareness of it among clinicians, PAPA syndrome is hard to diagnose and easy to be misdiagnosed or miss-diagnosed clinically.In this paper, twin brothers with recurrent fever and joints pain were reported.They were diagnosed with suppurative arthritis by many hospitals, but after receiving the joint cavity incision operation with negative pressure drainage for several times and antibiotics treatment, their conditions were not improved.Genetic tests showed that the twin brothers had a heterozygous mutation p. E250K (NM003978.3; c.748 G>A; p.Glu250Lys) on the PSTPIP1 gene.They were finally diagnosed with PAPA syndrome and improved with glucocorticoid therapy.

In addition to the pyogenic arthritis, pyoderma gangrenosum and acne(PAPA) syndrome, autoinflammatory diseases caused by mutations in the proline-serine-threonine phosphatase interacting protein 1 (PSTPIP1) gene also include a group of clinical syndromes such as PSTPIP1-associated myeloid-related proteinemia inflammatory(PAMI) and pyoderma gangrenosum, acne, and hidradenitis suppurativa(PASH) syndrome.In this paper, the expanded spectrum and clinical characteristics of PSTPIP1 related autoinflammatory diseases were reviewed, so as to deepen clinicians′ understanding of this disease, facilitate early diagnosis, and finally improve the prognosis of patients.