Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC). METHODS: We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively. RESULTS: The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis. CONCLUSIONS: In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.

Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors.

OBJECTIVES: This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT). METHODS: Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis. RESULTS: The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model. CONCLUSIONS: The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning. KEY POINTS: • A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.

Diagnostic nomogram for closed-loop small bowel obstruction requiring emergency surgery.

PURPOSE: This study aimed to build a diagnostic model of closed-loop small bowel obstruction (CL-SBO) using clinical information, blood test results, and computed tomography (CT) findings. METHODS: All patients who were diagnosed with small bowel obstruction (SBO) and underwent surgery between January 1, 2018, and October 31, 2021, in the affiliated hospital of Qingdao university were reviewed, and their relevant preoperative information was collected. All variables were selected using univariate analysis and backward stepwise regression to build a diagnostic nomogram model. K-fold cross-validation and bootstrap resampling techniques were used for internal validation, and data from Qingdao Central Hospital were used for external validation. We also evaluated the diagnostic performance of each CT finding and performed subgroup analysis according to bowel ischemia in the closed-loop small bowel obstruction (CL-SBO) group. RESULTS: A total of 219 patients (95 in the CL-SBO group and 124 in the open-loop small bowel obstruction [OL-SBO] group) were included in our research. D-dimers (median 1085 vs. 690, P = 0.019), tenderness (77.9% vs. 59.7%, P = 0.004), more than one beak sign (65.3% vs. 30.6%, P < 0.001), radial distribution (18.9% vs. 6.5%, P = 0.005), whirl sign (35.8% vs. 8.9%, P < 0.001), and ascites (71.6% vs. 53.2%, P = 0.006) were selected as the predictive variables of the nomogram. This model's Harrell's C statistic was 0.786 (95% confidence interval (CI), 0.724-0.848), and the Brier score was 0.182. The Harrell's C statistic of external validation was 0.784 (95%CI, 0.664-0.905); the Brier score was 0.190. Regarding the CT findings, radial distribution, U/C-shaped loop, and whirl sign had high specificity (93.5%, 96.0%, and 91.1%, respectively), but low sensitivity (18.9%, 8.4%, and 35.8%, respectively). D-dimer levels and tenderness were also associated with bowel ischemia. CONCLUSION: The nomogram accurately predicted CL-SBO in patients with SBO, and surgery should be considered when patients have a high risk for developing CL-SBO.

Giant cell tumors of the mobile spine with invasion of adjacent vertebrae: an unusual imaging finding.

BACKGROUND: Giant cell tumors of the mobile spine invasion of the adjacent vertebrae are an ignored imaging finding. METHODS: Nine patients with giant cell tumors of the mobile spine with invasion of the adjacent vertebrae confirmed by pathology were enrolled. Eight patients had pure giant cell tumors (GCTs), while one patient also had an aneurysmal bone cyst. All patients underwent conventional computed tomography, three-dimensional reconstruction, and conventional magnetic resonance imaging, while seven patients also underwent post-contrast magnetic resonance imaging. RESULTS: All patients showed GCTs of the mobile spine that arose from the vertebral body and extended to the vertebral arch. The tumors showed soft-tissue attenuation with no evidence of a mineralized matrix. Pathological fracture was seen in five patients. The margin of the original tumor showed partial sclerosis in four patients and involved an adjacent vertebral body with a sclerotic rim in two patients. The tumors showed a homogeneous and similar signal intensity to the normal spinal cord on T1WI (T1-weighted image) in five patients. The cystic area of the tumors was hyperintense on T2WI in the remaining four patients, while one patient showed hemorrhage that was hyperintense on T1WI. The solid components of the GCTs show marked enhancement in all cases, while the cystic area of the tumors was observed without enhancement on contrast-enhanced images in four patients. Bone destruction of the adjacent vertebral body showed a homogeneous signal on T1WI and T2WI and marked enhancement on contrast-enhanced images. CONCLUSIONS: Giant cell tumors of the mobile spine with invasion into adjacent vertebrae are an unusual imaging finding. Radiologists should be familiar with this imaging characteristic.

Concordance Between Watson for Oncology and a Multidisciplinary Clinical Decision-Making Team for Gastric Cancer and the Prognostic Implications: Retrospective Study.

BACKGROUND: With the increasing number of cancer treatments, the emergence of multidisciplinary teams (MDTs) provides patients with personalized treatment options. In recent years, artificial intelligence (AI) has developed rapidly in the medical field. There has been a gradual tendency to replace traditional diagnosis and treatment with AI. IBM Watson for Oncology (WFO) has been proven to be useful for decision-making in breast cancer and lung cancer, but to date, research on gastric cancer is limited. OBJECTIVE: This study compared the concordance of WFO with MDT and investigated the impact on patient prognosis. METHODS: This study retrospectively analyzed eligible patients (N=235) with gastric cancer who were evaluated by an MDT, received corresponding recommended treatment, and underwent follow-up. Thereafter, physicians inputted the information of all patients into WFO manually, and the results were compared with the treatment programs recommended by the MDT. If the MDT treatment program was classified as "recommended" or "considered" by WFO, we considered the results concordant. All patients were divided into a concordant group and a nonconcordant group according to whether the WFO and MDT treatment programs were concordant. The prognoses of the two groups were analyzed. RESULTS: The overall concordance of WFO and the MDT was 54.5% (128/235) in this study. The subgroup analysis found that concordance was less likely in patients with human epidermal growth factor receptor 2 (HER2)-positive tumors than in patients with HER2-negative tumors (P=.02). Age, Eastern Cooperative Oncology Group performance status, differentiation type, and clinical stage were not found to affect concordance. Among all patients, the survival time was significantly better in concordant patients than in nonconcordant patients (P<.001). Multivariate analysis revealed that concordance was an independent prognostic factor of overall survival in patients with gastric cancer (hazard ratio 0.312 [95% CI 0.187-0.521]). CONCLUSIONS: The treatment recommendations made by WFO and the MDT were mostly concordant in gastric cancer patients. If the WFO options are updated to include local treatment programs, the concordance will greatly improve. The HER2 status of patients with gastric cancer had a strong effect on the likelihood of concordance. Generally, survival was better in concordant patients than in nonconcordant patients.

Distribution patterns of microcalcifications in suspected thyroid carcinoma: a classification method helpful for diagnosis.

PURPOSE: The aim of this study was to compare the distribution patterns of microcalcifications in thyroid cancers with benign cases. METHODS: In total, 358 patients having microcalcifications on ultrasonography were analysed. Microcalcifications were categorised according to the distribution patterns: (I) microcalcifications inside one (a) or more (b) suspected nodules, (II) microcalcifications not only inside but also surrounding a suspected single (a) or multiple (b) nodules, and (III) focal (a) or diffuse (b) microcalcifications in the absence of any suspected nodule. Differences in distribution patterns of microcalcifications in benign and malignant thyroid lesions were compared. RESULTS: We found that the distribution patterns of microcalcifications differed between malignant (n = 325) and benign lesions (n = 117) (X 2 = 9.926, p < 0.01). Benign lesions were classified as type Ia (66.7%), type Ib (29.1%) or type IIIa (4.3%). The specificity of type II and type IIIb in diagnosing malignant cases was 100%. Among malignant lesions, 172 locations were classified as type Ia, 106 as type Ib, 12 as type IIa, 7 as IIb, 7 as type IIIa and 19 as type IIIb. Accompanying Hashimoto thyroiditis was most frequent in type III (51.6%). CONCLUSIONS: Types II and IIIb are highly specific for cancer detection. Microcalcifications outside a nodule and those detected in the absence of any nodule should therefore be reviewed carefully in clinical practice. KEY POINTS: • A method to classify distribution patterns of thyroid microcalcifications is presented. • Distribution features of microcalcifications are useful for diagnosing thyroid cancers. • Microcalcifications outside a suspicious nodule are highly specific for thyroid cancers. • Microcalcifications without suspicious nodules should also alert the physician to thyroid cancers.

Prevention of peripherally inserted central line-associated blood stream infections in very low-birth-weight infants by using a central line bundle guideline with a standard checklist: a case control study.

BACKGROUNDS: Catheter-related infections (CRIs) are one of the severe complications of PICC placement. If treatment is not timely or correct, the incidence of infection and mortality rate can be high. A central line bundle (CLB) guideline was first proposed by the Institute for Healthcare Improvement, and included five key measures. Very low-birth-weight infants (VLBWIs) have a low immune response and indistinct symptoms after infection compared with other populations (Costa P, Kimura AF, de Vizzotto MP, de Castro TE, West A, Dorea E. Prevalence and reasons for non-elective removal of peripherally inserted central catheter in neonates. Rev Gaucha Enferm. 2012;33:126-33). Some reviews have focused on the effect and safety of a CLB in VLBWIs and its preventive effect on bacterial colonization and infection. METHODS: Fifty-seven VLBWIs who underwent PICC insertion at a hospital in Qingdao, China, between November 2012 and June 2013, and for whom a CLB guideline and a standard checklist were adopted, were included in the CLB group. In contrast, 53 VLBWIs who underwent PICC insertion, but for whom a CLB guideline and a standard checklist were not adopted, were included in the control group. The incidence of CRIs was compared between before and after the treatment. RESULTS: The incidence of infection showed a statistically significant reduction from 10.0 to 2.20 per 1000 catheter days in the control group (P < 0.05). The incidence of catheter-related bloodstream infections decreased from 3.1 to 0 per 1000 catheter days, and that of colonization infections decreased from 6.9 to 2.2 per 1000 catheter days (P < 0.05), both of which indicated a statistically significant difference. The indwelling catheter time was 24.8 ± 7.4 days in the control group and 31.9 ± 15.0 days in the study group (P < 0.05), and these values were significantly different. CONCLUSION: The use of a CLB guideline with a standard checklist could be effective and feasible for preventing CRIs in VLBWIs and prolonging indwelling catheter time.

A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver.

BACKGROUND: The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC) in the non-cirrhotic liver. METHODS: A total of 156 patients with FNH (n = 55) and HCC (n = 101) were divided into a training set (n = 119) and a validation set (n = 37). Radiomics features were extracted from triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm, and a radiomics score (Rad-score) was calculated. Clinical data and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed by multivariate logistic regression analysis. Nomogram performance was assessed with respect to discrimination and clinical usefulness. RESULTS: Four thousand two hundred twenty-seven features were extracted and reduced to 10 features as the most important discriminators to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.964; 95% confidence interval [CI], 0.934-0.995) and the validation set (AUC, 0.865; 95% CI, 0.725-1.000). Age, Hepatitis B virus infection, and enhancement pattern were the independent clinical factors. The radiomics nomogram, which incorporated the Rad-score and clinical factors, showed good discrimination in the training set (AUC, 0.979; 95% CI, 0.959-0.998) and the validation set (AUC, 0.917; 95% CI, 0.800-1.000), and showed better discrimination capability (P < 0.001) compared with the clinical factors model (AUC, 0.799; 95% CI, 0.719-0.879) in the training set. Decision curve analysis showed the nomogram outperformed the clinical factors model in terms of clinical usefulness. CONCLUSIONS: The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating FNH from HCC in the non-cirrhotic liver, which might facilitate clinical decision-making process.