Explore the vast range of titles available.

Purpose Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient’s prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters. Methods HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction. Results Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients. Conclusion The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient’s prognosis.

Background Clinically differentiating preinvasive lesions (atypical adenomatous hyperplasia, AAH and adenocarcinoma in situ, AIS) from invasive lesions (minimally invasive adenocarcinomas, MIA and invasive adenocarcinoma, IA) manifesting as ground-glass opacity nodules (GGOs) is difficult due to overlap of morphological features. Hence, the current study was performed to explore the diagnostic efficiency of radiomics in assessing the invasiveness of lung adenocarcinoma manifesting as GGOs. Methods A total of 1018 GGOs pathologically confirmed as lung adenocarcinoma were enrolled in this retrospective study and were randomly divided into a training set (n = 712) and validation set (n = 306). The nodules were delineated manually and 2446 intra-nodular and peri-nodular radiomic features were extracted. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Clinical and semantic computerized tomography (CT) feature model, radiomic model and a combined nomogram were constructed and compared. Decision curve analysis (DCA) was used to evaluate the clinical value of the established nomogram. Results 16 radiomic features were selected and used for model construction. The radiomic model exhibited significantly better performance (AUC = 0.828) comparing to the clinical-semantic model (AUC = 0.746). Further analysis revealed that peri-nodular radiomic features were useful in differentiating between preinvasive and invasive lung adenocarcinomas appearing as GGOs with an AUC of 0.808. A nomogram based on lobulation sign and radiomic features showed the best performance (AUC = 0.835), and was found to have potential clinical value in assessing nodule invasiveness. Conclusions Radiomic model based on both intra-nodular and peri-nodular features showed good performance in differentiating between preinvasive lung adenocarcinoma lesions and invasive ones appearing as GGOs, and a nomogram based on clinical, semantic and radiomic features could provide clinicians with added information in nodule management and preoperative evaluation.

INTRODUCTION:Linear-array endoscopic ultrasound (EUS) and narrow-band imaging (NBI) are both used to estimate the invasion depth of nonpedunculated rectal lesions (NPRLs). However, it is unclear which procedure is more accurate. This randomized controlled trial aimed to compare the diagnostic accuracy of linear EUS and NBI for estimating the invasion depth of NPRLs.METHODS:This study is a single-center, randomized, tandem trial. Eligible patients with NPRLs were randomly assigned to A group (assessment with EUS followed by NBI) or B group (assessment with NBI followed by EUS). The invasion depth of each lesion was independently measured by each procedure and categorized as mucosal to slight submucosal (M-SMs, invasion depth <1,000 μm) or deep submucosal (SMd, invasion depth ≥1,000 μm) invasion, with postoperative pathology as the standard of measurement. The primary outcome was diagnostic accuracy, and secondary outcomes included sensitivity, specificity, and procedure time.RESULTS:Eighty-six patients with NPRLs were enrolled, and 79 patients were finally analyzed, including 39 cases in the A group and 40 cases in the B group. Comparable diagnostic accuracies were observed between EUS and NBI (96.2% vs 93.7%, P = 0.625). EUS identified lesions with deep submucosal invasion with 81.8% sensitivity while that of NBI was 63.6% (P = 0.500). The specificity of both EUS and NBI was 98.5%. The procedure time was also similar between EUS and NBI (5.90 ± 3.44 vs 6.4 ± 3.94 minutes, P = 0.450). Furthermore, the combined use of EUS and NBI did not improve diagnostic accuracy compared with EUS or NBI alone (94.9% vs 96.2% vs 93.7%, P = 0.333).DISCUSSION:Linear EUS and NBI measure the invasion depth of NPRLs with comparable accuracy. The combination of the 2 methods does not improve the diagnostic accuracy. Single NBI should be preferred, considering its simplicity and convenience in clinical practice.

Background Endoscopic ultrasonography (EUS) has become a reliable method for predicting the invasion depth of early gastric cancer (EGC). This study evaluated the accuracy of EUS in identifying lesions meeting expanded-indication criteria for endoscopic submucosal dissection (ESD) and analyzed clinicopathologic factors influencing the diagnostic accuracy of EUS in assessing tumor invasion depth. Methods This study investigated 542 EGCs of 515 patients who underwent EUS pretreatment. The pretreatment EUS-determined diagnosis was compared with the final histopathologic evaluation of resected specimens, and the impact of various clinicopathologic parameters on diagnostic accuracy was analyzed. Results The diagnostic accuracy of EUS in identifying lesions meeting expanded-indication criteria for ESD was 87.8% (259/295) for differentiated adenocarcinoma (D-type) 30 mm in diameter or smaller, 43.5% (10/23) for D-type tumor larger than 30 mm in diameter, and 75% (42/56) for undifferentiated adenocarcinoma (UD-type) 20 mm in diameter or smaller. Using multivariate analysis, the diagnostic accuracy of EUS in predicting tumor invasion depth was determined to be decreased significantly by ulcerous change and large tumor size (diameter, ≥30 mm). Conclusion For patients with EGC, D-type lesions 30 mm in diameter or smaller and UD-type lesions 20 mm in diameter or smaller can be diagnosed with high accuracy by EUS, but larger D-type lesions (diameter, >30 mm) should be considered carefully in terms of EUS-based treatment decisions. Findings of ulceration and large tumors are associated with incorrect diagnosis of tumor invasion depth by EUS.

Purpose The purpose of the article is to compare the diagnostic performance about radiation dose and image quality of low-dose CT with iterative reconstruction algorithm (iDose4) and standard-dose CT in the assessment of mesorectal fascia (MRF) invasion in rectal cancer patients. Materials and methods Ninety-one patients with biopsy-proven primary rectal adenocarcinoma underwent CT staging: 42 underwent low-dose CT, 49 underwent standard CT protocol. Low-dose contrast-enhanced MDCT scans were performed on a 256 (ICT, Philips) scanner using 120 kV, automated mAs modulation, iDose4 iterative reconstruction algorithm. Standard-dose MDCT scans were performed on the same scanner with 120 kV, 200–300 mAs. All patients underwent a standard lower abdomen MR study (on 1.5T magnet), including multiplanar sequences, considered as reference standard. Diagnostic accuracy of MRF assessment was determined on CT images for both CT protocols and compared with MRI images. Dose-length product (DLP) and CT dose index (CTDI) calculated for both groups were compared and statistically analyzed. Results Low-dose protocol with iDose4 showed high diagnostic quality in assessment of MRF with significant reduction (23%; p  = 0.0081) of radiation dose (DLP 2453.47) compared to standard-dose examination (DLP 3194.32). Conclusions Low-dose protocol combined with iDose4 reconstruction algorithm offers high-quality images, obtaining significant radiation dose reduction, useful in the evaluation of MRF involvement in rectal cancer patients.

To correlate histopathologic findings from biopsy specimens with their corresponding location within enhancing areas, non-enhancing areas and necrotic areas on contrast enhanced T1-weighted MRI scans (cT1). In 37 patients with newly diagnosed glioblastoma who underwent stereotactic biopsy, we obtained a correlation of 561 1mm3 biopsy specimens with their corresponding position on the intraoperative cT1 image at 1.5 Tesla. Biopsy points were categorized as enhancing (CE), non-enhancing (NE) or necrotic (NEC) on cT1 and tissue samples were categorized as \"viable tumor cells\", \"blood\" or \"necrotic tissue (with or without cellular component)\". Cell counting was done semi-automatically. NE had the highest content of tissue categorized as viable tumor cells (89% vs. 60% in CE and 30% NEC, respectively). Besides, the average cell density for NE (3764 ± 2893 cells/mm2) was comparable to CE (3506 ± 3116 cells/mm2), while NEC had a lower cell density with 2713 ± 3239 cells/mm2. If necrotic parts and bleeds were excluded, cell density in biopsies categorized as \"viable tumor tissue\" decreased from the center of the tumor (NEC, 5804 ± 3480 cells/mm2) to CE (4495 ± 3209 cells/mm2) and NE (4130 ± 2817 cells/mm2). The appearance of a glioblastoma on a cT1 image (circular enhancement, central necrosis, peritumoral edema) does not correspond to its diffuse histopathological composition. Cell density is elevated in both CE and NE parts. Hence, our study suggests that NE contains considerable amounts of infiltrative tumor with a high cellularity which might be considered in resection planning.

Objective To evaluate CT and histopathologic features of lung adenocarcinoma with pure ground-glass nodule (pGGN) ≤10 mm in diameter. Methods CT appearances of 148 patients (150 lesions) who underwent curative resection of lung adenocarcinoma with pGGN ≤10 mm (25 atypical adenomatous hyperplasias, 42 adenocarcinoma in situs, 38 minimally invasive adenocarcinomas, and 45 invasive pulmonary adenocarcinomas) were analyzed for lesion size, density, bubble-like sign, air bronchogram, vessel changes, margin, and tumour-lung interface. CT characteristics were compared among different histopathologic subtypes. Univariate and multivariate analysis were used to assess the relationship between CT characteristics of pGGN and lesion invasiveness, respectively. Results There were statistically significant differences among histopathologic subtypes in lesion size, vessel changes, and tumour-lung interface ( P <0.05). Univariate analysis revealed significant differences of vessel changes, margin and tumour-lung interface between preinvasive and invasive lesions ( P <0.05). Logistic regression analysis showed that the vessel changes, unsmooth margin and clear tumour-lung interface were significant predictive factors for lesion invasiveness, with odds ratios (95% CI) of 2.57 (1.17-5.62), 1.83 (1.25-2.68) and 4.25 (1.78-10.14), respectively. Conclusion Invasive lesions are found in 55.3% of subcentimeter pGGNs in our cohort. Vessel changes, unsmooth margin, and clear lung-tumour interface may indicate the invasiveness of lung adenocarcinoma with subcentimeter pGGN. Key points • Invasive lesions were found in 55.3% of lung adenocarcinomas with subcentimeter pGGNs • Lesion size, vessel changes, and tumour-lung interface showed different among histopathologic subtypes • Vessel changes, unsmooth margin and clear tumour-lung interface were predictors for lesion invasiveness

PurposeTo retrospectively determine the diagnostic values of vesical imaging reporting and data system (VI-RADS) score for detecting muscle-invasive bladder tumors.MethodsThis study included 297 consecutive patients with 339 tumors who previously diagnosed and subsequently underwent multiparametric MR imaging between January 2015 and March 2019. Two radiologists assessed the scores of muscle-invasive tumors using cutoff values of ≥ 4 and ≥ 3. Cutoff values for VI-RADS scores were estimated from the best operating points of the areas under the receiver operating characteristic curve analyses using the Youden J statistic. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated to assess the utility of VI-RADS for diagnosing muscle-invasive tumors.ResultsInter-observer agreement was excellent for three different MR imaging type at lesion level (k = 0.89 for T2W, k = 0.82 for DW, and k = 0.85 for DCE). At a cutoff value of 4, T2W and DW imaging had a diagnostic accuracy of 79.3% (269/339) for tumor lesions with muscle invasion, which was similar to an overall score of 80.2% (272/339). The overall VI-RAD score showed 80.2% accuracy (272/339), with a cutoff value of ≥ 4, yielding 91.3% sensitivity (85/93), 76.0% specificity (187/246), 83.3% PPV (85/102), and 78.9% NPV (187/237). When we considered an arbitrary overall score of ≥ 3 as the cutoff value, the accuracy was 63.7% (216/339); sensitivity, 94.6% (125/132); specificity, 43.9% (91/207); PPV, 51.6% (125/242); and NPV, 63.7% (91/97).ConclusionVI-RADS has an overall good performance in the diagnosis of muscle-invasive tumors.

Purpose This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. Methods We included a total of 68 PNI patients and 80 LVI patients who underwent surgical resection and pathological confirmation of rectal cancer. According to the PNI/LVI status, patients were divided into PNI positive group ( n  = 39), the PNI negative group ( n  = 29), LVI positive group ( n  = 48), and the LVI negative group ( n  = 32). External validation included a total of 42 patients with nerve and vascular invasion in patients with surgically resected and pathologically confirmed rectal cancer at another healthcare facility, with a PNI positive group ( n  = 32) and a PNI-negative group ( n  = 10) as well as an LVI positive group ( n  = 35) and LVI-negative group ( n  = 7). All patients underwent 3.0T magnetic resonance T1WI enhanced scanning. We use Firevoxel software to delineate the region of interest (ROI), extract histogram parameters, and perform univariate analysis, LASSO regression, and multivariate logistic regression analysis in sequence to screen for the best predictive factors. Then, we constructed a clinical prediction model and plotted it into a column chart for personalized prediction. Finally, we evaluate the performance and clinical practicality of the model based on the area under curve (AUC), calibration curve, and decision curve. Results Multivariate logistic regression analysis found that variance and the 75th percentile were independent risk factors for PNI, while maximum and variance were independent risk factors for LVI. The clinical prediction model constructed based on the above factors has an AUC of 0.734 (95% CI: 0.591–0.878) for PNI in the training set and 0.731 (95% CI: 0.509–0.952) in the validation set; The training set AUC of LVI is 0.701 (95% CI: 0.561–0.841), and the validation set AUC is 0.685 (95% CI: 0.439–0.932). External validation showed an AUC of 0.722 (95% CI: 0.565–0.878) for PNI; and an AUC of 0.706 (95% CI: 0.481–0.931) for LVI. Conclusions This study indicates that the combination of enhanced T1WI full volume histogram and clinical prediction model can be used to predict the perineural and lymphovascular invasion status of rectal cancer before surgery, providing valuable reference information for clinical diagnosis.

PurposeTo investigate the performance of computed tomography (CT) in the local staging of colon cancer in different segments, with emphasis on parameters that have been found to be significant for rectal cancer, namely, extramural venous invasion (EMVI) and tumor deposits (TDs).MethodsCT and pathology data from 137 patients were independently reviewed by radiology and pathology teams. The performance of CT in categorizing a given patient into good, versus poor prognostic groups was assessed for each segment, as well as the presence of lymph nodes (LNs), TDs and EMVIs. Discordant cases were re-evaluated to determine potential sources of error. Elastic stain was applied for EMVI discordance.ResultsThe T staging accuracy was 80.2%. For T stage stratification, CT performed slightly better in the left colon, and the lowest accuracy was in the transverse colon. Under-staging was more common (in 12.4%), and most of the mis-staged cases were in sigmoid colon. According to the first comprehensive correlative analysis, the sensitivity, specificity, and accuracy of CT for detecting TDs were found to be 57.9%, 92.4%, 87.6%, respectively. These figures were 44.7%, 72.7%, and 63.5% for LN, and 58.5%, 82.1% and 73% for EMVI. The detection rate was better for multifocal EMVI. The detection rate was also comparable (although substantially underestimated) for LNs, with the half of the LNs missed by CT being < 5 mm. Four patients that were classified as TD by CT, disclosed to be LNs by pathology. Correlative analysis led to refinement of the pathology criteria, with subsequent modifications of the initial reports in 13 (9.5%) patients.ConclusionOverall, CT performed well in the evaluation of colon cancer, as did TD and EMVI. It is advisable to include these parameters in CT-based staging. Radiologists should be aware of the pitfalls that occur more commonly in different segments.