Sparganosis is a parasitic infestation caused by sparganum, a plerocercoid tapeworm larva of the genus Spirometra. Since the first case of human sparganosis reported in 1908, sparganosis has been a global disease, and is common in China, Japan, and Southeast Asian countries. Consumption of raw snakes, frogs, fish, or drinking contaminated beverages are sources of human infections. Human sparganosis usually manifests in subcutaneous fat in areas such as the abdomen, genitourinary tract, and limbs. Breast sparganosis cases are rare, representing less than 2% of total cases of human infections. Complete surgical extraction of the sparganum is the treatment of choice. Because of the rarity of the disease, clinical suspicion is vital to reach the diagnosis of breast sparganosis. Here we report 2 rare cases of breast sparganosis presenting with a painless breast lump, both treated with surgical excision and sparganum extraction.
Abdomen ; Asian Continental Ancestry Group ; Beverages ; Breast ; Cestoda ; China ; Diagnosis ; Drinking ; Extremities ; Humans ; Japan ; Larva ; Snakes ; Sparganosis ; Sparganum ; Spirometra ; Subcutaneous Fat

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of “pre-PTA” shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, GradCAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

Objective: To assess the diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 treatment response algorithm (TRA) for the evaluation of hepatocellular carcinoma (HCC) treated with transarterial radioembolization. Materials and Methods: This retrospective study included patients who underwent transarterial radioembolization for HCC followed by hepatic surgery between January 2011 and December 2019. The resected lesions were determined to have either complete (100%) or incomplete (< 100%) necrosis based on histopathology. Three radiologists independently reviewed the CT or MR images of pre- and post-treatment lesions and assigned categories based on the LI-RADS version 2018 and the TRA, respectively. Diagnostic performances of LI-RADS treatment response (LR-TR) viable and nonviable categories were assessed for each reader, using histopathology from hepatic surgeries as a reference standard. Inter-reader agreements were evaluated using Fleiss κ. Results: A total of 27 patients (mean age ± standard deviation, 55.9 ± 9.1 years; 24 male) with 34 lesions (15 with complete necrosis and 19 with incomplete necrosis on histopathology) were included. To predict complete necrosis, the LR-TR nonviable category had a sensitivity of 73.3–80.0% and a specificity of 78.9–89.5%. For predicting incomplete necrosis, the LR-TR viable category had a sensitivity of 73.7–79.0% and a specificity of 93.3–100%. Five (14.7%) of 34 treated lesions were categorized as LR-TR equivocal by consensus, with two of the five lesions demonstrating incomplete necrosis. Interreader agreement for the LR-TR category was 0.81 (95% confidence interval: 0.66–0.96). Conclusion The LI-RADS version 2018 TRA can be used to predict the histopathologic viability of HCCs treated with transarterial radioembolization.

Objective: To assess the diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 treatment response algorithm (TRA) for the evaluation of hepatocellular carcinoma (HCC) treated with transarterial radioembolization. Materials and Methods: This retrospective study included patients who underwent transarterial radioembolization for HCC followed by hepatic surgery between January 2011 and December 2019. The resected lesions were determined to have either complete (100%) or incomplete (< 100%) necrosis based on histopathology. Three radiologists independently reviewed the CT or MR images of pre- and post-treatment lesions and assigned categories based on the LI-RADS version 2018 and the TRA, respectively. Diagnostic performances of LI-RADS treatment response (LR-TR) viable and nonviable categories were assessed for each reader, using histopathology from hepatic surgeries as a reference standard. Inter-reader agreements were evaluated using Fleiss κ. Results: A total of 27 patients (mean age ± standard deviation, 55.9 ± 9.1 years; 24 male) with 34 lesions (15 with complete necrosis and 19 with incomplete necrosis on histopathology) were included. To predict complete necrosis, the LR-TR nonviable category had a sensitivity of 73.3–80.0% and a specificity of 78.9–89.5%. For predicting incomplete necrosis, the LR-TR viable category had a sensitivity of 73.7–79.0% and a specificity of 93.3–100%. Five (14.7%) of 34 treated lesions were categorized as LR-TR equivocal by consensus, with two of the five lesions demonstrating incomplete necrosis. Interreader agreement for the LR-TR category was 0.81 (95% confidence interval: 0.66–0.96). Conclusion The LI-RADS version 2018 TRA can be used to predict the histopathologic viability of HCCs treated with transarterial radioembolization.

Multiple primary malignancies are defined as the presence of more than one malignant neoplasm with a distinct histology occurring at different sites in the same individual. They are classified as synchronous or metachronous according to the diagnostic time interval of different malignancies. Diagnosis of multiple primary malignancies should avoid misclassification from multifocal/multicentric tumors or recurrent/metastatic lesions.In multiple primary malignancies, with increase in the number of primary tumors, the frequency rapidly decreases. Here, we report an exceptionally rare case of a woman who was diagnosed with metachronous sporadic sextuple primary malignancies including bilateral breast cancers (gastric cancer, ovarian Sertoli-Leydig cell tumor, left breast cancer, thyroid cancer, right breast cancer, and rectal neuroendocrine tumor). The sextuple primary malignancies in this case involved 5 different organs: the stomach, ovary, thyroid, rectum, and bilateral breasts. Further studies are needed to elucidate the current epidemiologic status of patients with multiple primary malignancies.

Background: Few studies have evaluated the use of automated artificial intelligence (AI)-based pain recognition in postoperative settings or the correlation with pain intensity. In this study, various machine learning (ML)-based models using facial expressions, the analgesia nociception index (ANI), and vital signs were developed to predict postoperative pain intensity, and their performances for predicting severe postoperative pain were compared. Methods: In total, 155 facial expressions from patients who underwent gastrectomy were recorded postoperatively; one blinded anesthesiologist simultaneously recorded the ANI score, vital signs, and patient self-assessed pain intensity based on the 11-point numerical rating scale (NRS). The ML models’ area under the receiver operating characteristic curves (AUROCs) were calculated and compared using DeLong’s test. Results: ML models were constructed using facial expressions, ANI, vital signs, and different combinations of the three datasets. The ML model constructed using facial expressions best predicted an NRS ≥ 7 (AUROC 0.93) followed by the ML model combining facial expressions and vital signs (AUROC 0.84) in the test-set. ML models constructed using combined physiological signals (vital signs, ANI) performed better than models based on individual parameters for predicting NRS ≥ 7, although the AUROCs were inferior to those of the ML model based on facial expressions (all P < 0.050). Among these parameters, absolute and relative ANI had the worst AUROCs (0.69 and 0.68, respectively) for predicting NRS ≥ 7. Conclusions The ML model constructed using facial expressions best predicted severe postoperative pain (NRS ≥ 7) and outperformed models constructed from physiological signals.