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.

Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images.

Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images is viewer-dependent and may vary between radiologists and medical institutions. In this study, we aimed to develop a fully automatic method to segment sacroiliac joint (SIJ) and further grading diagnose sacroiliitis associated with AS on CT. We studied 435 CT examinations from patients with AS and control at two hospitals. No-new-UNet (nnU-Net) was used to segment the SIJ, and a 3D convolutional neural network (CNN) was used to grade sacroiliitis with a three-class method, using the grading results of three veteran musculoskeletal radiologists as the ground truth. We defined grades 0-I as class 0, grade II as class 1, and grades III-IV as class 2 according to modified New York criteria. nnU-Net segmentation of SIJ achieved Dice, Jaccard, and relative volume difference (RVD) coefficients of 0.915, 0.851, and 0.040 with the validation set, respectively, and 0.889, 0.812, and 0.098 with the test set, respectively. The areas under the curves (AUCs) of classes 0, 1, and 2 using the 3D CNN were 0.91, 0.80, and 0.96 with the validation set, respectively, and 0.94, 0.82, and 0.93 with the test set, respectively. 3D CNN was superior to the junior and senior radiologists in the grading of class 1 for the validation set and inferior to expert for the test set (P < 0.05). The fully automatic method constructed in this study based on a convolutional neural network could be used for SIJ segmentation and then accurately grading and diagnosis of sacroiliitis associated with AS on CT images, especially for class 0 and class 2. The method for class 1 was less effective but still more accurate than that of the senior radiologist.

[CT and MRI fusion based on generative adversarial network and convolutional neural networks under image enhancement].

Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, Q AB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.

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.

A comparative study of effective atomic number calculations for dual-energy CT.

PURPOSE: Several new formalisms of Effective Atomic Number ( Z eff ) have emerged recently, deviating from the widely accepted Mayneord's definition. This comparative study aims to reexamine their theories, reveal their connections, and apply them to material differentiation on dual-energy computed tomography (DECT). METHODS: The first part of this paper is an in-depth review of several highly cited Z eff formalisms. This part includes (1) refuting the claim in Taylor's study that the classic Mayneord's formalism was inaccurate, (2) showing that Mayneord's, Rutherford's, and Bourque's formalisms were equivalent, and (3) explaining the fundamental difference between Taylor's and Bourque's formalisms. The second part of this paper explains how we translated the theories into software implementation and added an open-source Z eff calculation engine to our free research software 3D Quantitative Imaging (3DQI). The work includes developing an interpolation method based on radial basis function to make Taylor's formalism applicable to DECT, and devising a table lookup method to generate Z eff map with high efficiency for all appropriate formalisms. RESULTS: Comparing Bourque's and Taylor's formalisms for six common materials over 40 ∼ 100 keV energy range, it was found that Bourque's Z eff values had a weak energy dependence by 0.18% ∼ 3.10%, but for Taylor's results this variation increased by a factor of 10. Further comparison showed that at 61 keV, different formalisms fall into two categories-Bourque, Mayneord, Van Abbema (a derivative of Rutherford) for the first category, and Taylor and Manohara for the second. Formalisms within each category produced similar Z eff values. For a material consisting of two elements, the two categories of formalisms tended to show a greater discrepancy if the constituent elements had larger difference in Z . The developed Z eff calculation engine was successfully applied to kidney stone classification and colon electronic cleansing. CONCLUSIONS: We renewed the understanding of several popular Z eff formalisms: Contrary to the conclusion of Taylor's study, Mayneord's power-law formula is well grounded in theory; Bourque's formalism (based on the average electron microscopic cross-section) is considered numerically equivalent to Rutherford's, but with the advantage of being mathematically rigorous and physically meaningful; Taylor's formalism (based on the average atomic microscopic cross-section) is theoretically not suitable for DECT but a workaround still exists; Manohara's formalism should be used with caution due to a problem in its definition of electron cross-sections. The developed Z eff engine in the 3DQI software facilitated accurate and efficient Z eff estimate for various DECT applications.

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.