An External Validation Study for Automated Segmentation of Vestibular Schwannoma.

OBJECTIVE: To validate how an automated model for vestibular schwannoma (VS) segmentation developed on an external homogeneous dataset performs when applied to internal heterogeneous data. PATIENTS: The external dataset comprised 242 patients with previously untreated, sporadic unilateral VS undergoing Gamma Knife radiosurgery, with homogeneous magnetic resonance imaging (MRI) scans. The internal dataset comprised 10 patients from our institution, with heterogeneous MRI scans. INTERVENTIONS: An automated VS segmentation model was developed on the external dataset. The model was tested on the internal dataset. MAIN OUTCOME MEASURE: Dice score, which measures agreement between ground truth and predicted segmentations. RESULTS: When applied to the internal patient scans, the automated model achieved a mean Dice score of 61% across all 10 images. There were three tumors that were not detected. These tumors were 0.01 ml on average (SD = 0.00 ml). The mean Dice score for the seven tumors that were detected was 87% (SD = 14%). There was one outlier with Dice of 55%-on further review of this scan, it was discovered that hyperintense petrous bone had been included in the tumor segmentation. CONCLUSIONS: We show that an automated segmentation model developed using a restrictive set of siloed institutional data can be successfully adapted for data from different imaging systems and patient populations. This is an important step toward the validation of automated VS segmentation. However, there are significant shortcomings that likely reflect limitations of the data used to train the model. Further validation is needed to make automated segmentation for VS generalizable.

Comparison of Diatrizoate and Iohexol for Patient Acceptance and Fecal-Tagging Performance in Noncathartic CT Colonography.

OBJECTIVE: The aim of this study was to compare diatrizoate and iohexol regarding patient acceptance and fecal-tagging performance in noncathartic computed tomography colonography. METHODS: This study enrolled 284 volunteers with fecal tagging by either diatrizoate or iohexol at an iodine concentration of 13.33 mg/mL and an iodine load of 24 g. Patient acceptance was rated on a 4-point scale of gastrointestinal discomfort. Two gastrointestinal radiologists jointly analyzed image quality, fecal-tagging density and homogeneity, and residual contrast agent in the small intestine. The results were compared by the generalized estimating equation method. RESULTS: Patient acceptance was comparable between the 2 groups (3.95 ± 0.22 vs 3.96 ± 0.20, P = 0.777). The diatrizoate group had less residual fluid and stool than the iohexol group ( P = 0.019, P = 0.004, respectively). There was no significant difference in colorectal distention, residual fluid, and stool tagging quality between the 2 groups (all P 's > 0.05). The mean 2-dimensional image quality score was 4.59 ± 0.68 with diatrizoate and 3.60 ± 1.14 with iohexol ( P < 0.001). The attenuation of tagged feces was 581 ± 66 HU with diatrizoate and 1038 ± 117 HU with iohexol ( P < 0.001). Residual contrast agent in the small intestine was assessed at 55.3% and 62.3% for the diatrizoate group and iohexol group, respectively ( P = 0.003). CONCLUSIONS: Compared with iohexol, diatrizoate had better image quality, proper fecal-tagging density, and more homogeneous tagging along with comparable excellent patient acceptance, and might be more suitable for fecal tagging in noncathartic computed tomography colonography.

Influence of Data Distribution on Federated Learning Performance in Tumor Segmentation.

Purpose: To investigate the correlation between differences in data distributions and federated deep learning (Fed-DL) algorithm performance in tumor segmentation on CT and MR images. Materials and Methods: Two Fed-DL datasets were retrospectively collected (from November 2020 to December 2021): one dataset of liver tumor CT images (Federated Imaging in Liver Tumor Segmentation [or, FILTS]; three sites, 692 scans) and one publicly available dataset of brain tumor MR images (Federated Tumor Segmentation [or, FeTS]; 23 sites, 1251 scans). Scans from both datasets were grouped according to site, tumor type, tumor size, dataset size, and tumor intensity. To quantify differences in data distributions, the following four distance metrics were calculated: earth mover's distance (EMD), Bhattacharyya distance (BD), χ2 distance (CSD), and Kolmogorov-Smirnov distance (KSD). Both federated and centralized nnU-Net models were trained by using the same grouped datasets. Fed-DL model performance was evaluated by using the ratio of Dice coefficients, θ, between federated and centralized models trained and tested on the same 80:20 split datasets. Results: The Dice coefficient ratio (θ) between federated and centralized models was strongly negatively correlated with the distances between data distributions, with correlation coefficients of -0.920 for EMD, -0.893 for BD, and -0.899 for CSD. However, KSD was weakly correlated with θ, with a correlation coefficient of -0.479. Conclusion: Performance of Fed-DL models in tumor segmentation on CT and MRI datasets was strongly negatively correlated with the distances between data distributions.Keywords: CT, Abdomen/GI, Liver, Comparative Studies, MR Imaging, Brain/Brain Stem, Convolutional Neural Network (CNN), Federated Deep Learning, Tumor Segmentation, Data Distribution Supplemental material is available for this article. © RSNA, 2023See also the commentary by Kwak and Bai in this issue.

Vestibular dysfunction in neurofibromatosis type 2-related schwannomatosis.

Neurofibromatosis type 2-related schwannomatosis is a genetic disorder characterized by neurologic tumours, most typically vestibular schwannomas that originate on the vestibulo-cochlear nerve(s). Although vestibular symptoms can be disabling, vestibular function has never been carefully analysed in neurofibromatosis type 2-related schwannomatosis. Furthermore, chemotherapy (e.g. bevacizumab) can reduce tumour volume and improve hearing in neurofibromatosis type 2-related schwannomatosis, but nothing is known about its vestibular effects. In this report, we studied the three primary vestibular-mediated behaviours (eye movements, motion perception and balance), clinical vestibular disability (dizziness and ataxia), and imaging and hearing in eight untreated patients with neurofibromatosis type 2-related schwannomatosis and compared their results with normal subjects and patients with sporadic, unilateral vestibular schwannoma tumours. We also examined how bevacizumab affected two patients with neurofibromatosis type 2-related schwannomatosis. Vestibular schwannomas in neurofibromatosis type 2-related schwannomatosis degraded vestibular precision (inverse of variability, reflecting a reduced central signal-to-noise ratio) but not vestibular accuracy (amplitude relative to ideal amplitude, reflecting the central signal magnitude) and caused clinical disability. Bevacizumab improved vestibular precision and clinical disability in both patients with neurofibromatosis type 2-related schwannomatosis but did not affect vestibular accuracy. These results demonstrate that vestibular schwannoma tumours in our neurofibromatosis type 2-related schwannomatosis population degrade the central vestibular signal-to-noise ratio, while bevacizumab improves the signal-to-noise ratio, changes that can be explained mechanistically by the addition (schwannoma) and suppression (bevacizumab) of afferent neural noise.

COVID and Cancer: A Complete 3D Advanced Radiological CT-Based Analysis to Predict the Outcome.

BACKGROUND: Cancer patients infected with COVID-19 were shown in a multitude of studies to have poor outcomes on the basis of older age and weak immune systems from cancer as well as chemotherapy. In this study, the CT examinations of 22 confirmed COVID-19 cancer patients were analyzed. METHODOLOGY: A retrospective analysis was conducted on 28 cancer patients, of which 22 patients were COVID positive. The CT scan changes before and after treatment and the extent of structural damage to the lungs after COVID-19 infection was analyzed. Structural damage to a lung was indicated by a change in density measured in Hounsfield units (HUs) and by lung volume reduction. A 3D radiometric analysis was also performed and lung and lesion histograms were compared. RESULTS: A total of 22 cancer patients were diagnosed with COVID-19 infection. A repeat CT scan were performed in 15 patients after they recovered from infection. Most of the study patients were diagnosed with leukemia. A secondary clinical analysis was performed to show the associations of COVID treatment on the study subjects, lab data, and outcome on mortality. It was found that post COVID there was a decrease of >50% in lung volume and a higher density in the form of HUs due to scar tissue formation post infection. CONCLUSION: It was concluded that COVID-19 infection may have further detrimental effects on the lungs of cancer patients, thereby, decreasing their lung volume and increasing their lung density due to scar formation.

Ten-Year Follow-up of Internal Neurofibroma Growth Behavior in Adult Patients With Neurofibromatosis Type 1 Using Whole-Body MRI.

BACKGROUND AND OBJECTIVES: Internal neurofibromas, including plexiform neurofibromas (PNF), can cause significant morbidity in patients with neurofibromatosis type 1 (NF1). PNF growth is most pronounced in children and young adults, with more rapid growth thought to occur in a subset of PNF termed distinct nodular lesions (DNL). Growth behavior of internal neurofibromas and DNL in older adults is not well documented; yet knowledge thereof is important for patient risk stratification and clinical trial design. The primary objective of this study was to evaluate the long-term growth behavior of internal neurofibromas in adults with NF1. Secondary objectives were to correlate tumor growth behavior with patient-specific, tumor-specific, and patient-reported variables. METHODS: In this prospective cohort study, internal neurofibromas were identified on coronal short TI inversion recovery sequences on baseline and follow-up whole-body MRIs (WBMRIs). Tumor growth and shrinkage were defined as a volume change ≥20%. The association between tumor growth and patient-specific (baseline age, sex, and genotype), tumor-specific (morphology, location, DNL presence on baseline WBMRI, and maximum standardized uptake value on baseline PET imaging), and patient-reported variables (endogenous and exogenous hormone exposure, pain intensity, and quality of life) was assessed using the Spearman correlation coefficient and Kruskal-Wallis test. RESULTS: Of 106 patients with a baseline WBMRI obtained as part of a previous research study, 44 had a follow-up WBMRI. Three additional patients with WBMRIs acquired for clinical care were included, generating 47 adults for this study. The median age during baseline WBMRI was 42 years (range 18-70). The median time between WBMRIs was 10.4 years. Among 324 internal neurofibromas, 62.8% (56% of PNF and 62.1% of DNL) shrank spontaneously without treatment and 17.1% (17.9% of PNF and 13.8% of DNL) grew. Growth patterns were heterogeneous within participants. Patient-specific, tumor-specific, and patient-reported variables (including endogenous and exogenous hormone exposure) were not strong predictors of tumor growth. DISCUSSION: Internal neurofibroma growth behavior in older adults differs fundamentally from that in children and young adults, with most tumors, including DNL, demonstrating spontaneous shrinkage. Better growth models are needed to understand factors that influence tumor growth. These results will inform clinical trial design for internal neurofibromas.

Change in humeral anchor position significantly affects isometry in ulnar collateral ligament repair: a 3-dimensional computer modeling study.

BACKGROUND: Medial ulnar collateral ligament (UCL) repair utilization is increasing in recent years, bolstered by shorter rehabilitation and satisfactory clinical outcomes. Although previous literature has illustrated the importance of tunnel position on restoring graft isometry in reconstruction, there remains a paucity of literature guiding anchor placement in UCL repair. The purpose of this study is to design a 3-dimensional (3D) elbow model to understand the effect of anchor location on UCL repair isometry. METHODS: A 3D computer model of an elbow joint was created using computed tomographic and magnetic resonance imaging MRI scans from a single patient. The humeral and ulnar attachments of the UCL were plotted using 3 methodologies: (1) geometric cloud mapping and (2) quantitative measurements from the anatomic studies by Camp et al and (3) Frangiamore et al. A 3.5-mm-diameter clockface was placed on each attachment site, which allowed for simulation of 12 distinct 1.75-mm deviations in anchor position. The 3 models were ranged through 0°-120° at 10° increments, and the 3D distances were measured between the ligament centroids. The humeral and ulnar anchors were sequentially repositioned around the clockfaces, and construct lengths were again measured to evaluate changes in isometry. A paired Student t test was performed to determine if there was a significant difference in isometry between the humeral and ulnar anchor deviations. RESULTS: Using method 1, the UCL repair length at 90° of elbow flexion was 26.8 mm. This construct underwent 13.6 mm of total excursion for a 46.4% change in length throughout its arc of motion. Method 2 produced a 19.3-mm construct that underwent 0.8 mm of excursion for a 3.9% length change throughout the arc. Method 3 produced a 24.5-mm construct that underwent 2.3 mm of excursion for a 9.4% length change. Identifying ligament footprints using the quantitative anatomic measurements from Camp et al and Frangiamore et al improved construct isometry through 120° of flexion (length changes of 3.9% and 9.4%, respectively) when compared to using the geometric cloud technique alone (46.4% length change). Humeral anchor deviations produced a significant increase in repair construct excursion compared with ulnar anchor deviations (P < .001). CONCLUSION: When performing UCL repair, small deviations in humeral anchor position may significantly influence ligament repair isometry. Using quantitative anatomic data may help identify anchor positions with improved repair isometry. Particularly when addressing detachments of the humeral footprint, surgeons should be critical of the humeral anchor position in order to restore native anatomy and optimal biomechanics.

Management of neurofibromatosis type 1-associated plexiform neurofibromas.

Plexiform Neurofibromas (PN) are a common manifestation of the genetic disorder neurofibromatosis type 1 (NF1). These benign nerve sheath tumors often cause significant morbidity, with treatment options limited historically to surgery. There have been tremendous advances over the past two decades in our understanding of PN, and the recent regulatory approvals of the MEK inhibitor selumetinib are reshaping the landscape for PN management. At present, there is no agreed upon PN definition, diagnostic evaluation, surveillance strategy, or clear indications for when to initiate treatment and selection of treatment modality. In this review, we address these questions via consensus recommendations from a panel of multidisciplinary NF1 experts.

The miR-23b/27b/24-1 Cluster Inhibits Hepatic Fibrosis by Inactivating Hepatic Stellate Cells.

BACKGROUND & AIMS: Hepatic fibrosis is characterized by hepatic stellate cell (HSC) activation and transdifferentiation-mediated extracellular matrix (ECM) deposition, which both contribute to cirrhosis. However, no antifibrotic regimen is available in the clinic. microRNA-23b/27b/24-1 cluster inhibition of transforming growth factor-ß (TGF-ß) signaling during hepatic development prompted us to explore whether this cluster inhibits HSC activation and hepatic fibrosis. METHODS: Experimental fibrosis was studied in carbon tetrachloride (CCl4)-treated C57BL/6 mice. After administration of miR-23b/27b/24-1 lentivirus or vehicle, animals were euthanized for liver histology. In primary rat HSC and HSC-T6, the anti-fibrotic effect of miR-23b/27b/24-1 cluster was furtherly investigated by RNA-sequencing, luciferase reporter assay, western blotting and bioinformatic means. RESULTS: In this study, we showed that increasing the miR-23b/27b/24-1 level through intravenous delivery of miR-23b/27b/24-1 lentivirus ameliorated mouse hepatic fibrosis. Mechanistically, the miR-23b/27b/24-1 cluster directly targeted messenger RNAs, which reduced the protein expression of 5 secretory profibrotic genes (TGF-ß2, Gremlin1, LOX, Itgα2, and Itgα5) in HSCs. Suppression of the TGF-ß signaling pathway by down-regulation of TGF-ß2, Itgα2, and Itgα5, and activation of the bone morphogenetic protein signaling pathway by inhibition of Gremlin1, decreased extracellular matrix secretion of HSCs. Furthermore, down-regulation of LOX expression softened the ECM. Moreover, a reduction in tissue inhibitors of metalloproteinase 1 expression owing to weakened TGF-ß signaling increased ECM degradation. CONCLUSIONS: Hepatic overexpression of the miR-23b/27b/24-1 cluster blocked hepatic fibrosis and may be a novel therapeutic regimen for patients with hepatic fibrosis.

DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI.

Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical location and heterogeneous appearance on MRI. In this study, wepropose deep interactive networks (DINs) to address the above limitations. User interactions guide the model to recognize complicated tumors and quickly adapt to heterogeneous tumors. We introduce a simple but effective Exponential Distance Transform (ExpDT) that converts user interactions into guide maps regarded as the spatial and appearance prior. Comparing with popular Euclidean and geodesic distances, ExpDT is more robust to various image sizes, which reserves the distribution of interactive inputs. Furthermore, to enhance the tumor-related features, we design a deep interactive module to propagate the guides into deeper layers. We train and evaluate DINs on three MRI data sets from NF1 patients. The experiment results yield significant improvements of 44% and 14% in DSC comparing with automated and other interactive methods, respectively. We also experimentally demonstrate the efficiency of DINs in reducing user burden when comparing with conventional interactive methods.

Radiomics Analysis of Gd-EOB-DTPA Enhanced Hepatic MRI for Assessment of Functional Liver Reserve.

Rationale and Objectives To evaluate the effectiveness of radiomics analysis based on Gd-EOB-DTPA enhanced hepatic MRI for functional liver reserve assessment in HCC patients. Materials and Methods Radiomics features were extracted from Gd-EOB-DTPA enhanced MRI images in 60 HCC patients. Boruta algorithm was performed to select features associated with indocyanine green retention rate at 15 min (ICG R15). Prediction and classification model were built by performing Random Forest regression analysis. Pearson correlation analysis and AUC of ROC were used to assess performance of the two models. Results A total of 165 radiomics features were extracted. Six radiomics features were selected to build the prediction model. A Predicted value of ICG R15 for each patient was calculated by the prediction model. Pearson correlation analysis revealed that predicted values were significantly associated with actual values of ICG R15 (R value = 0.90, p < 0.001). Nine radiomics features were selected to build the classification model. AUC of ROC revealed favorable performance of the classification model for identifying patients with ICG R15 <10% (AUC: 0.906, 95%CI: 0.900-0.913), <15% (AUC: 0.954, 95%CI: 0.950-0.958), and <20% (AUC: 0.996, 95%CI: 0.995-0.996). Conclusion Radiomics analysis of Gd-EOB-DTPA enhanced hepatic MRI can be used for assessment of functional liver reserve in HCC patients.

Role of 3D Volumetric and Perfusion Imaging for Detecting Early Changes in Pancreatic Adenocarcinoma.

PURPOSE: There is a major shortage of reliable early detection methods for pancreatic cancer in high-risk groups. The focus of this preliminary study was to use Time Intensity-Density Curve (TIDC) and Marley Equation analyses, in conjunction with 3D volumetric and perfusion imaging to demonstrate their potential as imaging biomarkers to assist in the early detection of Pancreatic Ductal Adenocarcinoma (PDAC). EXPERIMENTAL DESIGNS: A quantitative retrospective and prospective study was done by analyzing multi-phase Computed Tomography (CT) images of 28 patients undergoing treatment at different stages of pancreatic adenocarcinoma using advanced 3D imaging software to identify the perfusion and radio density of tumors. RESULTS: TIDC and the Marley Equation proved useful in quantifying tumor aggressiveness. Perfusion delays in the venous phase can be linked to Vascular Endothelial Growth Factor (VEGF)-related activity which represents the active part of the tumor. 3D volume analysis of the multiphase CT scan of the patient showed clear changes in arterial and venous perfusion indicating the aggressive state of the tumor. CONCLUSION: TIDC and 3D volumetric analysis can play a significant role in defining the response of the tumor to treatment and identifying early-stage aggressiveness.

Loss of NEIL3 activates radiotherapy resistance in the progression of prostate cancer.

OBJECTIVE: To explore the genetic changes in the progression of castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC) and the reason why these cancers resist existing therapies. METHODS: We employed our CRPC cell line microarray and other CRPC or NEPC datasets to screen the target gene NEIL3. Lentiviral transfection and RNA interference were used to construct overexpression and knockdown cell lines. Cell and animal models of radiotherapy were established by using a medical electron linear accelerator. Flow cytometry was used to detect apoptosis or cell cycle progression. Western blot and qPCR were used to detect changes in the protein and RNA levels. RESULTS: TCGA and clinical patient datasets indicated that NEIL3 was downregulated in CRPC and NEPC cell lines, and NEIL3 was correlated with a high Gleason score but a good prognosis. Further functional studies demonstrated that NEIL3 had no effect on the proliferation and migration of PCa cells. However, cell and animal radiotherapy models revealed that NEIL3 could facilitate the radiotherapy sensitivity of PCa cells, while loss of NEIL3 activated radiotherapy resistance. Mechanistically, we found that NEIL3 negatively regulated the expression of ATR, and higher NEIL3 expression repressed the ATR/CHK1 pathway, thus regulating the cell cycle. CONCLUSIONS: We demonstrated that NEIL3 may serve as a diagnostic or therapeutic target for therapy-resistant patients.

B7 score and T cell infiltration stratify immune status in prostate cancer.

BACKGROUND: Although immune checkpoint inhibitors (ICIs), especially programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) axis blockers, exhibit prominent antitumor effects against numerous malignancies, their benefit for patients with prostate cancer (PCa) has been somewhat marginal. This study aimed to assess the feasibility of B7-H3 or HHLA2 as alternative immunotherapeutic targets in PCa. METHODS: Immunohistochemistry was performed to evaluate the expression pattern of PD-L1, B7-H3 and HHLA2 and the infiltration of CD8+ and Foxp3+ lymphocytes in 239 PCa tissues from two independent cohorts. The correlations between B7-H3 and HHLA2 and clinicopathological features, including the presence of CD8+ and Foxp3+ tumor-infiltrating lymphocytes (TILs), were explored. RESULTS: HHLA2 expression was much higher than PD-L1 expression but lower than B7-H3 expression in PCa tissues. High expression of both B7-H3 and HHLA2 was significantly associated with higher Gleason score and tumor stage, lymph node metastasis and dismal overall survival (OS) and cancer-specific survival (CSS). Moreover, a high B7 score, defined as high B7-H3 expression and/or high HHLA2 expression, was an independent prognostic predictor for PCa. Of note, a high B7 score was negatively correlated with CD8+ TILs. Importantly, a new immune classification, based on the B7 score and CD8+ TILs, successfully stratified OS and CSS in PCa. CONCLUSIONS: Both B7-H3 and HHLA2 have a critical impact on the immunosuppressive microenvironment, and the B7 score could be used as an independent prognostic factor for PCa. The B7 score combined with CD8+ TILs could be used as a new immune classification to stratify the risk of death, especially cancer-related death, for patients with PCa. These findings may provide insights that could improve response to immune-related comprehensive therapy for PCa in the future.

Exploring the Interobserver Agreement in Computer-Aided Radiologic Tumor Measurement and Evaluation of Tumor Response.

The accurate, objective, and reproducible evaluation of tumor response to therapy is indispensable in clinical trials. This study aimed at investigating the reliability and reproducibility of a computer-aided contouring (CAC) tool in tumor measurements and its impact on evaluation of tumor response in terms of RECIST 1.1 criteria. A total of 200 cancer patients were retrospectively collected in this study, which were randomly divided into two sets of 100 patients for experiential learning and testing. A total of 744 target lesions were identified by a senior radiologist in distinctive body parts, of which 278 lesions were in data set 1 (learning set) and 466 lesions were in data set 2 (testing set). Five image analysts were respectively instructed to measure lesion diameter using manual and CAC tools in data set 1 and subsequently tested in data set 2. The interobserver variability of tumor measurements was validated by using the coefficient of variance (CV), the Pearson correlation coefficient (PCC), and the interobserver correlation coefficient (ICC). We verified that the mean CV of manual measurement remained constant between the learning and testing data sets (0.33 vs. 0.32, p = 0.490), whereas it decreased for the CAC measurements after learning (0.24 vs. 0.19, p < 0.001). The interobserver measurements with good agreement (CV < 0.20) were 29.9% (manual) vs. 49.0% (CAC) in the learning set (p < 0.001) and 30.9% (manual) vs. 64.4% (CAC) in the testing set (p < 0.001). The mean PCCs were 0.56 ± 0.11 mm (manual) vs. 0.69 ± 0.10 mm (CAC) in the learning set (p = 0.013) and 0.73 ± 0.07 mm (manual) vs. 0.84 ± 0.03 mm (CAC) in the testing set (p < 0.001). ICCs were 0.633 (manual) vs. 0.698 (CAC) in the learning set (p < 0.001) and 0.716 (manual) vs. 0.824 (CAC) in the testing set (p < 0.001). The Fleiss' kappa analysis revealed that the overall agreement was 58.7% (manual) vs. 58.9% (CAC) in the learning set and 62.9% (manual) vs. 74.5% (CAC) in the testing set. The 80% agreement of tumor response evaluation was 55.0% (manual) vs. 66.0% in the learning set and 60.6% (manual) vs. 79.7% (CAC) in the testing set. In conclusion, CAC can reduce the interobserver variability of radiological tumor measurements and thus improve the agreement of imaging evaluation of tumor response.

Risk stratification of thymic epithelial tumors by using a nomogram combined with radiomic features and TNM staging.

OBJECTIVES: To construct and validate a nomogram model that integrated the CT radiomic features and the TNM staging for risk stratification of thymic epithelial tumors (TETs). METHODS: A total of 136 patients with pathology-confirmed TETs who underwent CT examination were collected from two institutions. According to the WHO pathological classification criteria, patients were classified into low-risk and high-risk groups. The TNM staging was determined in terms of the 8th edition AJCC/UICC staging criteria. LASSO regression was performed to extract the optimal features correlated to risk stratification among the 704 radiomic features calculated. A nomogram model was constructed by combining the Radscore and the TNM staging. The clinical performance was evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was employed for survival analysis. RESULTS: Five optimal features identified by LASSO regression were employed to calculate the Radscore correlated to risk stratification. The nomogram model showed a better performance in both training cohort (AUC = 0.84, 95%CI 0.75-0.91) and external validation cohort (AUC = 0.79, 95%CI 0.69-0.88). The calibration curve and DCA analysis indicated a better accuracy of the nomogram model for risk stratification than either Radscore or the TNM staging alone. The KM analysis showed a significant difference between the two groups stratified by the nomogram model (p = 0.02). CONCLUSIONS: A nomogram model that integrated the radiomic signatures and the TNM staging could serve as a reliable model of risk stratification in predicting the prognosis of patients with TETs. KEY POINTS: • The radiomic features could be associated with the TET pathophysiology. • TNM staging and Radscore could independently stratify the risk of TETs. • The nomogram model is more objective and more comprehensive than previous methods.

The metastatic promoter DEPDC1B induces epithelial-mesenchymal transition and promotes prostate cancer cell proliferation via Rac1-PAK1 signaling.

Metastasis is the major cause of prostate cancer (PCa)-related mortality. Epithelial-mesenchymal transition (EMT) is a vital characteristic feature that empowers cancer cells to adapt and survive at the beginning of metastasis. Therefore, it is essential to identify the regulatory mechanism of EMT in metastatic prostate cancer (mPCa) and to develop a novel therapy to block PCa metastasis. Here, we discovered a novel PCa metastasis oncogene, DEP domain containing 1B (DEPDC1B), which was positively correlated with the metastasis status, high Gleason score, advanced tumor stage, and poor prognosis. Functional assays revealed that DEPDC1B enhanced the migration, invasion, and proliferation of PCa cells in vitro and promoted tumor metastasis and growth in vivo. Mechanistic investigations clarified that DEPDC1B induced EMT and enhanced proliferation by binding to Rac1 and enhancing the Rac1-PAK1 pathway. This DEPDC1B-mediated oncogenic effect was reversed by a Rac1-GTP inhibitor or Rac1 knockdown. In conclusion, we discover that the DEPDC1B-Rac1-PAK1 signaling pathway may serve as a multipotent target for clinical intervention in mPCa.

Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study.

BACKGROUND: Existing studies have demonstrated that imaging parameters may affect radiomic features. However, the influence of feature calculating parameters has been overlooked. The purpose of this study is to investigate the influence of feature calculating parameters (gray-level range and bin size) on the reproducibility of CT radiomic features. METHODS: Thirty-six CT scans from an anthropomorphic thoracic phantom were acquired with different imaging parameters including effective dose, pitch, slice thicknesses and reconstruction kernels. The influence of feature calculating parameters was investigated in terms of three gray-level ranges and eleven gray-level bin sizes. Feature reproducibility was assessed by the intraclass correlation coefficient (ICC) with the cutoff value of 0.8 and the coefficient of variation (CV) with the cutoff value of 20%. The agreements of reproducible features in different ranges and bin sizes were analyzed by Kendall's W test and Kappa test. The proportions of reproducible features, in terms of two calculating, four imaging and two segmentation parameters, were evaluated using Cochran's Q test and Dunn's test. RESULTS: For the three gray-level ranges, 50% (44/88) of features were reproducible with a perfect agreement (Kendall's W coefficient 0.844, P<0.001). Of the 72 features that may be influenced by gray-level bin size, 33.3% (24/72) were reproducible for 11 bin sizes with a perfect agreement (Kendall's W coefficient 0.879, P<0.001). For the proportions of reproducible features, there was no statistically significant difference among three ranges (P=0.420), but there was among eleven bin sizes (P=0.013). The proportions of reproducible features in feature calculating parameters were statistically significantly lower than those in imaging parameters (adjusted P<0.05). CONCLUSIONS: Feature calculating parameters may have a greater influence than imaging parameters on the reproducibility of CT radiomic features, which should be given special attention in clinical applications.

Immune Cytolytic Activity as an Indicator of Immune Checkpoint Inhibitors Treatment for Prostate Cancer.

Immune checkpoint inhibitors (ICIs) treatment is becoming a new hope for cancer treatment. However, most prostate cancer (PCa) patients do not benefit from it. In order to achieve the accuracy of ICIs treatment in PCa and reduce unnecessary costs for patients, we have analyzed the data from TCGA database to find a indicator that can assist the choice of treatment. By analyzing the data of PCa patients with TMB analysis and immune infiltration analysis, we found the expression of immune cells in different immune infiltration groups. Commonly used markers of ICIs, expressed on CD8+ T cell, were highly expressed in the high immune group. Then we used the forimmune cytolytic activity (CYT) to determine its relationship with the target of ICIs treatment. Through the analysis of CYT score and the ligands of immune checkpoints, we found that there was a significant correlation between them. With the increase of CYT score, the expression of CD80/86, PD-L1/L2, TNFSF14, and LGALS9 also increased gradually. Similarly, CD8+ T cells were significantly increased in the CYT high group compared with the CYT low group in PRAD. The present research provides novel insights into the immune microenvironment of PRAD and potential immunotherapies. The proposed CYT score is a clinically promising indicator that can serve as a marker to assist anti-PD-L1 or other ICIs treatment. At the same time, it also provides a basis for the selection of other immune checkpoint drugs.