Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study.

Background: Lymph node metastasis (LNM) is the most common route of metastasis for lung cancer, and it is an independent risk factor for long-term survival and recurrence in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in the differential diagnosis of LNM in part-solid nodules (PSNs) of NSCLC. Methods: A total of 955 patients with NSCLC confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to identify the risk factors for LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. Ten-fold cross-validation was used for model training and validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated, and the Delong test was used to compare the predictive performance between the models. Results: LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were identified as the independent risk factors for LNM. The image predictors model established by the four independent factors of CT semantic features, except smoking history, showed a good diagnostic efficacy for LNM. The AUC in the validation group was 0.857, and the sensitivity, specificity, and accuracy of the model were all 77.6%. Conclusions: Preoperative CT semantic features have good diagnostic value for the LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type, and mean diameter demonstrated excellent diagnostic efficacy and can provide non-invasive evaluation in clinical practice.

Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.

Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer.

PURPOSE: To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer. METHODS: The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis. RESULTS: The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001). CONCLUSION: The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.

MRI-based radiomics for preoperative prediction of recurrence and metastasis in rectal cancer.

OBJECTIVES: To explore the value of multi-parametric MRI (mp-MRI) radiomic model for preoperative prediction of recurrence and/or metastasis (RM) as well as survival benefits in patients with rectal cancer. METHODS: A retrospective analysis of 234 patients from two centers with histologically confirmed rectal adenocarcinoma was conducted. All patients were divided into three groups: training, internal validation (in-vad) and external validation (ex-vad) sets. In the training set, radiomic features were extracted from T2WI, DWI, and contrast enhancement T1WI (CE-T1) sequence. Radiomic signature (RS) score was then calculated for feature screening to construct a rad-score model. Subsequently, preoperative clinical features with statistical significance were selected to construct a clinical model. Independent predictors from clinical and RS related to RM were selected to build the combined model and nomogram. RESULTS: After feature extraction, 26 features were selected to construct the rad-score model. RS (OR = 0.007, p < 0.01), MR-detected T stage (mrT) (OR = 2.92, p = 0.03) and MR-detected circumferential resection margin (mrCRM) (OR = 4.70, p = 0.01) were identified as independent predictors of RM. Then, clinical model and combined model were constructed. ROC curve showed that the AUC, accuracy, sensitivity and specificity of the combined model were higher than that of the other two models in three sets. Kaplan-Meier curves showed that poorer disease-free survival (DFS) time was observed for patients in pT3-4 stages with low RS score (p < 0.001), similar results were also found in pCRM-positive patients (p < 0.05). CONCLUSION: The mp-MRI radiomics model can be served as a noninvasive and accurate predictors of RM in rectal cancer that may support clinical decision-making.

Radiomics analysis of contrast-enhanced computerized tomography for differentiation of gastric schwannomas from gastric gastrointestinal stromal tumors.

PURPOSE: To assess the performance of radiomics-based analysis of contrast-enhanced computerized tomography (CE-CT) images for distinguishing GS from gastric GIST. METHODS: Forty-nine patients with GS and two hundred fifty-three with GIST were enrolled in this retrospective study. CT features were evaluated by two associate chief radiologists. Radiomics features were extracted from portal venous phase images using Pyradiomics software. A non-radiomics dataset (combination of clinical characteristics and radiologist-determined CT features) and a radiomics dataset were used to build stepwise logistic regression and least absolute shrinkage and selection operator (LASSO) logistic regression models, respectively. Model performance was evaluated according to sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve, and Delong's test was applied to compare the area under the curve (AUC) between different models. RESULTS: A total of 1223 radiomics features were extracted from portal venous phase images. After reducing dimensions by calculating Pearson correlation coefficients (PCCs), 20 radiomics features, 20 clinical characteristics + CT features were used to build the models, respectively. The AUC values for the models using radiomics features and those using clinical features were more than 0.900 for both the training and validation groups. There were no significant differences in predictive performance between the radiomic and clinical data models according to Delong's test. CONCLUSION: A radiomics-based model applied to CE-CT images showed comparable predictive performance to senior physicians in the differentiation of GS from GIST.

An unexpected encounter and outcome between endobronchial lipoma and carcinoma: a case report and literature review.

Bronchial lipoma is a rare benign tumor of the lung, which is often misdiagnosed due to concomitant pulmonary diseases. In addition, the coexistence of endobronchial lipoma and lung cancer is extremely unusual. To date, no related computed tomography (CT) images have been reported. The patient was a 53-year-old man, who was admitted to our hospital with cough, yellow phlegm, and fever for 1 week. The CT image showed an irregular mass in the medial segment of the right middle lobe (B4a) with surrounding ground glass opacity, and another solid nodule in the right lower lobe (B6b). Unfortunately, after 2 weeks of anti-inflammatory treatment, the bronchial invasion of the B4a nodule did not decrease significantly, so further bronchoscopy was carried out and tumor resection was performed using endoscopic mucosal resection with a ligation device (EMR-L). During the follow-up 4 months, it was found that the B6b nodule was marked enlargement and then removed. The lesions of the B4a and B6b were confirmed as endobronchial lipoma and squamous cell carcinoma (T1aN0M0) by histopathology and immunohistochemical staining, respectively, and no postoperative radiotherapy or chemotherapy was performed. Regrettably, after 29 months of follow-up, we observed recurrence and slow enlargement of the lipoma in its original location, progressive emphysema in both lungs, and solitary chest wall metastasis from the B6b squamous cell carcinoma that had been resected. Therefore, endobronchial endoscopy resection should be carefully selected for larger endobronchial lipoma. If it is accompanied by early squamous cell carcinoma (T1aN0M0), we still recommend active postoperative chemoradiotherapy.

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study.

BACKGROUND: To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. METHODS: Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. RESULTS: Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. CONCLUSION: The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer.

BACKGROUND: To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa). MATERIALS: The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient's prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated. RESULTS: Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly. CONCLUSION: The multiple easy-to-use deep learning-based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

The value of MRI for downgrading of breast suspicious lesions detected on ultrasound.

BACKGROUND: Most of suspicious lesions classified as breast imaging reporting and data system (BI-RADS) 4A and 4B categories on ultrasound (US) were benign, resulting in unnecessary biopsies. MRI has a high sensitivity to detect breast cancer and high negative predictive value (NPV) to exclude malignancy. The purpose of this study was to investigate the value of breast MRI for downgrading of suspicious lesions with BI-RADS 4A and 4B categories on US. METHODS: Patients who underwent breast MRI for suspicious lesions classified as 4A and 4B categories were included in this retrospective study. Two radiologists were aware of the details of suspicious lesions detected on US and evaluated MR images. MRI BI-RADS categories were given by consensus on the basis on dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Pathological results and imaging follow-up at least 12 months were used as a reference standard. Sensitivity, specificity, positive predictive value (PPV), NPV and their 95% confidence interval (CI) were calculated for MRI findings. RESULTS: One sixty seven patients with 186 lesions (US 4A category: 145, US 4B category: 41) consisted of the study cohort. The malignancy rate was 34.9% (65/186). On MRI, all malignancies showed true-positive results and 92.6% (112/121) benign lesions were correctly diagnosed. MRI increased PPV from 34.9% (65/186) to 87.8% (65/74) and reduced the false-positive biopsies by 92.6% (112/121). The sensitivity, specificity, PPV and NPV of MRI were 100% (95% CI: 94.5%-100%), 92.6% (95% CI: 86.3%-96.5%), 87.8% (95% CI: 78.2%-94.3%) and 100% (95% CI: 96.8%-100%), respectively. 2.2% (4/186) of suspicious lesions were additionally detected on MRI, 75% (3/4) of which were malignant. CONCLUSION: MRI could downgrade suspicious lesions classified as BI-RADS 4A and 4B categories on US and avoided unnecessary benign biopsies without missing malignancy. Additional suspicious lesions detected on MRI needed further work-up.

Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer.

OBJECTIVES: To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer. METHODS: This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T2-weighted images, diffusion-weighted images and enhanced T1-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance. RESULTS: The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice. CONCLUSION: A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.

Can DWI provide additional value to Kaiser score in evaluation of breast lesions.

OBJECTIVES: To explore added value of diffusion-weighted imaging (DWI) as an adjunct to Kaiser score (KS) for differentiation of benign from malignant lesions on breast magnetic resonance imaging (MRI). METHODS: Two hundred forty-six patients with 273 lesions (155 malignancies) were included in this retrospective study from January 2015 to December 2019. All lesions were proved by pathology. Two radiologists blind to pathological results evaluated lesions according to KS. Lesions with score > 4 were considered malignant. Four thresholds of ADC values -1.3 × 10-3mm2/s, 1.4 × 10-3mm2/s, 1.53 × 10-3mm2/s, and 1.6 × 10-3mm2/s were used to distinguish benign from malignant lesions. For combined diagnosis, a lesion with KS > 4 and ADC values below the preset cutoffs was considered as malignant; otherwise, it was benign. Sensitivity, specificity, and area under the curve (AUC) were compared between KS, DWI, and combined diagnosis. RESULTS: The AUC of KS was significantly higher than that of DWI alone (0.941 vs 0.901, p = 0.04). The sensitivity of KS (96.8%) and DWI (97.4 - 99.4%) was comparable (p > 0.05) while the specificity of KS (83.9%) was significantly higher than that of DWI (19.5-56.8%) (p < 0.05). Adding DWI as an adjunct to KS resulted in a 0-2.5% increase of specificity and a 0.1-1.3% decrease of sensitivity; however, the difference did not reach statistical significance (p > 0.05). CONCLUSION: KS showed higher diagnostic performance than DWI alone for discrimination of breast benign and malignant lesions. DWI showed no additional value to KS for characterizing breast lesions. KEY POINTS: • KS showed higher diagnostic performance than DWI alone for differentiation of benign from breast malignant lesions. • DWI alone showed a high sensitivity but a low specificity for characterizing breast lesions. • Diagnostic performance did not improve using DWI as an adjunct to KS.

Imaging manifestations of Caroli disease with autosomal recessive polycystic kidney disease: a case report and literature review.

BACKGROUND: Both Caroli disease (CD) and autosomal recessive polycystic kidney disease (ARPKD) are autosomal recessive disorders, which are more commonly found in infants and children, for whom surviving to adulthood is rare. Early diagnosis and intervention can improve the survival rate to some extent. This study adopted the case of a 26-year-old pregnant woman to explore the clinical and imaging manifestations and progress of CD concomitant with ARPKD to enable a better understanding of the disease. CASE PRESENTATION: A 26-year-old pregnant woman was admitted to our hospital for more than 2 months following the discovery of pancytopenia and increased creatinine. Ultrasonography detected an enlarged left liver lobe, widened hepatic portal vein, splenomegaly, and dilated splenic vein. In addition, both kidneys were obviously enlarged and sonolucent areas of varying sizes were visible, but color Doppler flow imaging revealed no abnormal blood flow signals. The gestational age was approximately 25 weeks, which was consistent with the actual fetal age. Polyhydramnios was detected but no other abnormalities were identified. Magnetic resonance imaging revealed that the liver was plump, and polycystic liver disease was observed near the top of the diaphragm. The T1 and T2 weighted images were the low and high signals, respectively. The bile duct was slightly dilated; the portal vein was widened; and the spleen volume was enlarged. Moreover, the volume of both kidneys had increased to an abnormal shape, with multiple, long, roundish T1 and T2 abnormal signals being observed. Magnetic resonance cholangiopancreatography revealed that intrahepatic cystic lesions were connected with intrahepatic bile ducts. The patient underwent a genetic testing, the result showed she carried two heterozygous mutations in PKHD1. The patient was finally diagnosed with CD with concomitant ARPKD. The baby underwent a genetic test three months after birth, the result showed that the patient carried one heterozygous mutations in PKHD1, which indicated the baby was a PKHD1 carrier. CONCLUSIONS: This case demonstrates that imaging examinations are of great significance for the diagnosis and evaluation of CD with concomitant ARPKD.

Gastric calcifying fibrous tumors: Computed tomography findings and clinical manifestations.

ABSTRACT: To retrospectively analyze the computed tomography (CT) findings and clinical manifestations of gastric calcifying fibrous tumor (CFTs).The features of 7 cases with pathologically proven gastric CFTs who had undergone CT were assessed, including tumor location, contour, growth, degree of enhancement, calcification and clinical data. In addition, the size and CT value of each lesion were measured. The mean values of these CT findings and clinical data were statistically analyzed only for continuous variables.Four patients were female and three were male (mean age: 33.3 years; range: 22 ∼ 47 years). Nonspecific clinical symptoms: abdominal pain and discomfort were observed in four cases and the CFTs were incidentally detected in the other three cases. Regarding tumor markers, lower ferritin levels were observed in three female patients. All of the gastric CFTs were solitary and mainly located inside the body; they were in round or oval shape and exhibited endophytic growth. Gastric CFTs are usually small sized and could contain confluent and coarse calcifications; cyst, necrosis, ulcer, bleeding and surrounding lymphadenopathy were not found in any of the cases. Unenhanced CT values of gastric CFTs were higher than those of same-transect soft tissue. Mild-to-moderate enhancement in the arterial phase and progressive enhancement in the portal venous phase were mainly noted.A gastric mass with a high unenhanced CT attenuation value, confluent and coarse calcifications and mild-to-moderate enhancement could prompt a diagnosis of gastric CFT. In addition, (1) being young- or middle-aged, (2) having relatively low ferritin levels, and (3) tumor located in the gastric body have critical reference value for diagnosis of gastric CFT.

Intrinsic brain abnormalities of irritable bowel syndrome with diarrhea: a preliminary resting-state functional magnetic resonance imaging study.

BACKGROUND: The aim of the present study was to explore the brain active characteristics of patients with irritable bowel syndrome with diarrhea (IBS-D) using resting-state functional magnetic resonance imaging technology. METHODS: Thirteen IBS-D patients and fourteen healthy controls (HC) were enrolled. All subjects underwent head MRI examination during resting state. A voxel-based analysis of fractional amplitude of low frequency fluctuation (fALFF) maps between IBS-D and HC was performed using a two-sample t-test. The relationship between the fALFF values in abnormal brain regions and the scores of Symptom Severity Scale (IBS-SSS) were analyzed using Pearson correlation analysis. RESULTS: Compared with HC, IBS-D patients had lower fALFF values in the left medial superior frontal gyrus and higher fALFF values in the left hippocampus and right precuneus. There was a positive correlation between the duration scores of IBS-SSS and fALFF values in the right precuneus. CONCLUSION: The altered fALFF values in the medial superior frontal gyri, left hippocampus and right precuneus revealed changes of intrinsic neuronal activity, further revealing the abnormality of gut-brain axis of IBS-D.

Intrauterine device found in an ovarian tumor: A case report.

RATIONALE: Intrauterine devices (IUDs) are one of the most common and effective methods of contraception worldwide. Migration of an IUD to an extrauterine site is a rare complication. The aim of this study was to report an extremely rare case in which an IUD was found in an ovarian tumor. PATIENT CONCERNS: A 63-year-old Chinese woman presented with vaginal bleeding and lower abdominal pain during hospitalization due to pneumonia. Preoperative imaging showed bilateral cystic masses in the adnexal region, and ring hyperdensity was found in the right ovarian mass. Endometrial thickening and multiple uterine leiomyomas were found on ultrasonography. Hysteroscopy showed partial septate uterus and a small endometrial polyp. DIAGNOSIS: Bilateral ovarian cystadenomas with perforation of the IUD into the right ovarian tumor were considered based on preoperative imaging and the patient's medical history. Furthermore, early endometrial carcinoma was suspected. INTERVENTIONS: The patient underwent hysterectomy, bilateral salpingo-oophorectomy, and omentectomy. A stainless steel ring IUD was confirmed within the right ovarian tumor during the operation. OUTCOMES: The pathology results demonstrated bilateral ovarian serous cystadenofibromas with focal epithelial proliferation and endometrial atypical hyperplasia with malignant transformation. The patient has been followed up for 7 months, and there has been no recurrence at present. LESSONS: The presence of an IUD within an ovarian tumor is extremely rare. This is the second reported case in the English literature describing an extrauterine IUD within an ovarian tumor. The correlation between ovarian cancer tumorigenesis and IUD translocation is unclear and requires further investigation.

Rab5a Promotes Cytolethal Distending Toxin B-Induced Cytotoxicity and Inflammation.

The cytolethal distending toxin B subunit (CdtB) induces significant cytotoxicity and inflammation in many cell types that are involved in the pathogenesis of postinfectious irritable bowel syndrome (PI-IBS). However, the underlying mechanisms remain unclear. This study tested the potential role of Rab small GTPase 5a (Rab5a) in the process. We tested mRNA and protein expression of proinflammatory cytokines (interleukin-1ß [IL-1ß] and IL-6) in THP-1 macrophages by quantitative PCR (qPCR) and enzyme-linked immunosorbent assays (ELISAs), respectively. In the primary colonic epithelial cells, Cdt treatment induced a CdtB-Rab5a-cellugyrin association. Rab5a silencing, by target small hairpin RNAs (shRNAs), largely inhibited CdtB-induced cytotoxicity and apoptosis in colon epithelial cells. CRISPR/Cas9-mediated Rab5a knockout also attenuated CdtB-induced colon epithelial cell death. Conversely, forced overexpression of Rab5a intensified CdtB-induced cytotoxicity. In THP-1 human macrophages, Rab5a shRNA or knockout significantly inhibited CdtB-induced mRNA expression and production of proinflammatory cytokines (IL-1ß and IL-6). Rab5a depletion inhibited activation of nuclear factor-κB (NF-κB) and Jun N-terminal protein kinase (JNK) signaling in CdtB-treated THP-1 macrophages. Rab5a appears essential for CdtB-induced cytotoxicity in colonic epithelial cells and proinflammatory responses in THP-1 macrophages.

Clinical and CT findings of COVID-19: differences among three age groups.

BACKGROUND: The novel coronavirus pneumonia (coronavirus disease 2019, COVID-19) has spread around the world. We aimed to recapitulate the clinical and CT imaging features of COVID-19 and their differences in three age groups. METHODS: The clinical and CT data of patients with COVID-19 (n = 307) that had been divided into three groups (Group 1: < 40 years old; Group 2: 40 ≤ age < 60 years old; Group 3: ≥ 60 years old) according to age were analyzed retrospectively. RESULTS: Of all patients, 114 (37.1%) had histories of epidemiological exposure, 48 (15.6%) were severe/critical cases, 31 had hypertension (10.1%), 15 had diabetes mellitus (4.9%), 3 had chronic obstructive pulmonary disease (COPD, 1%). Among the three groups, severe/critical type, hypertension and diabetes occurred more commonly in the elderly group compared with Group 1&2 (P < 0.05, respectively). Cough and chest tightness/pain were more commonly appeared in Group 2&3 compared with Group 1 (P < 0.05, respectively). Compared with Group 1 and 2, there were more abnormal laboratory examination indexes (including CRP increase, abnormal percentage of lymphocytes, neutrophils and monocytes) in Group 3 (P < 0.05, respectively). CT images revealed that more lobes were affected and more subpleural lesions were involved in the elderly group, besides, crazy paving sign, bronchodilatation and pleural thickening were more commonly seen in the elderly group, with significant difference between Group 1&2, Group 2&3 (P < 0.05, respectively). CONCLUSIONS: COVID-19 presented representative clinical manifestations, laboratory examinations and CT findings, but three age groups possessed their own specific characteristics. Grasping the clinical and CT features stratified by age will be helpful for early definite diagnosis of COVID-19.

Value of Apparent Diffusion Coefficient for Assessing Preoperative T Staging of Low Rectal Cancer and Whether This Is Correlated With Ki-67 Expression.

PURPOSE: To explore the value of the apparent diffusion coefficient (ADC) in assessing preoperative T staging of low rectal cancer and the correlation between ADC value and Ki-67 expression. METHODS: Data on 77 patients with a proven pathology of low rectal cancer were retrospectively analyzed. All patients underwent a magnetic resonance imaging scan 1 week prior to operation, and the mean ADC value was measured. All tumors were fully removed, and pathologic staging was determined. The Ki-67 expression was determined using immunohistochemical methods in all patients. The correlation between Ki-67 expression and ADC features was studied. RESULTS: A total of 77 patients with low rectal cancer were included in the study. The pathology type was adenocarcinoma. The numbers of patients with pathological stages T1, T2, T3, and T4 were 9, 23, 32, and 13, respectively. The ADC value of all tumors ranged from 0.60 to 1.20 mm2/s. The average Ki-67 proliferation index was 55.3% ± 20.2%. A significant difference was observed between the preoperative ADC value and pathological T staging of low rectal cancer (P < .01). The more advanced the T stage, the lower the detected ADC values were. A negative correlation was noted between the preoperative ADC value and Ki-67 proliferation index of rectal cancer (r = -0.71, P < .01). When the Ki-67 proliferation index increased, lower ADC values were detected. CONCLUSION: The ADC values can provide useful information on preoperative tumor staging and may facilitate evaluation of the biological behavior of low rectal cancer. The ADC values should be considered a sensitive image biomarker of rectal cancer.

Primary hepatic melanoma: A case report of computed tomography and magnetic resonance imaging findings.

RATIONALE: Malignant melanoma predominantly develops in middle-aged and older adults, most commonly occurring on the skin and rarely on internal organs. Malignant melanoma originating in the liver is extremely rare. Imaging findings of primary hepatic melanoma (PHM) are scarce in relevant literature. PATIENT CONCERNS: The patient was a 69-year-old woman from Zhejiang, China, who was admitted to the hospital because of upper abdominal pain that persisted for >10 days. DIAGNOSES: Computed tomography (CT) findings indicated the presence of a circular low-density shadow of approximately 7.5 × 8.0 cm in the hepatic hilar region. Magnetic resonance imaging (MRI) indicated a heterogeneous solid cystic mass in the hepatic hilar region. The mass exhibited heterogeneous low-signal intensity on a T1-weighted image (T1WI) and slightly higher signal intensity on a T2-weighted image (T2WI). The tumor appeared as multiple irregular strips with high-signal intensity on T1WI and low-signal intensity on T2WI. The diffusion-weighted image revealed increased signal intensity. The tumor continued to be enhanced after enhancement. Clinical data suggested that the tumor was a malignant liver tumor. INTERVENTIONS: The patient underwent a CT guide puncture hepatic biopsy. The tumor was located in the hepatic hilar region adjacent to the large blood vessels and invaded the portal vein. Because a resection was highly risky, conservative treatment was conducted. OUTCOMES: Postoperative pathology and clinical examination confirmed that the tumor was malignant PHM. The patient has been followed up for 6 months. The patient underwent CT reexamination 2 months after conservative treatment, the results of which revealed that the tumor progressed. Multiple lesions were identified; moreover, the tumor size had increased and the tumor had invaded the portal vein and intrahepatic bile duct. The patient was reexamined by CT in another hospital 6 months after conservative treatment. The results revealed peritoneal, omental metastases and multi bone metastases. LESSONS: To our best knowledge, this is the first reported case of a PHM with complete imaging data, including preoperative CT and MRI examinations and a follow-up CT examination. From compiling the CT and MRI findings of this patient and those of relevant studies, this study can serve as a reference for the preoperative diagnosis and differential diagnosis of PHM.

A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer.

PURPOSE: To explore the radiomics features of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC) based on X-ray mammography, and to differentiate the two groups of cases. MATERIALS AND METHODS: Preoperative mammograms of 120 patients with breast ductal carcinoma confirmed by surgical pathology were retrospectively analyzed, which include 30 TNBC and 90 non-TNBC patients. The manual segmentation of breast lesions was performed by ITK-SNAP software and 12 radiomics features were extracted by Omni-Kinetics software. The differences of these radiomics features between TNBC and non-TNBC groups were compared, and the receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of each radiomics parameter for differentiating TNBC from non-TNBC, and the corresponding area under the curve (AUC), sensitivity and specificity were obtained. RESULTS: There were statistically significant differences for 4 radiomics features between TNBC and non-TNBC datasets (P < 0.05). They were the roundness, concavity, gray average and skewness of breast lesions. Compared with non-TNBC, TNBC cases have following characteristics of (1) more round with the roundness of 0.621 vs. 0.413 (P < 0.001), (2) more regular with the concavity of 0.087 vs. 0.141 (P < 0.01), (3) higher density or gray average (67.261 vs. 56.842, P < 0.05), and (4) lower skewness (- 0.837 vs.- 0.671, P = 0.034). AUCs of ROC curves computed using features of the roundness and concavity were both larger than 0.70. CONCLUSION: Radiomics features based on X-ray mammography may be helpful to distinguish between TNBC and non-TNBC, which were associated with breast tumor histology.