Critical COVID-19, Victivallaceae abundance, and celiac disease: A mediation Mendelian randomization study.

Celiac disease exhibits a higher prevalence among patients with coronavirus disease 2019. However, the potential influence of COVID-19 on celiac disease remains uncertain. Considering the significant association between gut microbiota alterations, COVID-19 and celiac disease, the two-step Mendelian randomization method was employed to investigate the genetic causality between COVID-19 and celiac disease, with gut microbiota as the potential mediators. We employed the genome-wide association study to select genetic instrumental variables associated with the exposure. Subsequently, these variables were utilized to evaluate the impact of COVID-19 on the risk of celiac disease and its potential influence on gut microbiota. Employing a two-step Mendelian randomization approach enabled the examination of potential causal relationships, encompassing: 1) the effects of COVID-19 infection, hospitalized COVID-19 and critical COVID-19 on the risk of celiac disease; 2) the influence of gut microbiota on celiac disease; and 3) the mediating impact of the gut microbiota between COVID-19 and the risk of celiac disease. Our findings revealed a significant association between critical COVID-19 and an elevated risk of celiac disease (inverse variance weighted [IVW]: P = 0.035). Furthermore, we observed an inverse correlation between critical COVID-19 and the abundance of Victivallaceae (IVW: P = 0.045). Notably, an increased Victivallaceae abundance exhibits a protective effect against the risk of celiac disease (IVW: P = 0.016). In conclusion, our analysis provides genetic evidence supporting the causal connection between critical COVID-19 and lower Victivallaceae abundance, thereby increasing the risk of celiac disease.

Parity-time symmetry enabled ultra-efficient nonlinear optical signal processing.

Nonlinear optical signal processing (NOSP) has the potential to significantly improve the throughput, flexibility, and cost-efficiency of optical communication networks by exploiting the intrinsically ultrafast optical nonlinear wave mixing. It can support digital signal processing speeds of up to terabits per second, far exceeding the line rate of the electronic counterpart. In NOSP, high-intensity light fields are used to generate nonlinear optical responses, which can be used to process optical signals. Great efforts have been devoted to developing new materials and structures for NOSP. However, one of the challenges in implementing NOSP is the requirement of high-intensity light fields, which is difficult to generate and maintain. This has been a major roadblock to realize practical NOSP systems for high-speed, high-capacity optical communications. Here, we propose using a parity-time (PT) symmetric microresonator system to significantly enhance the light intensity and support high-speed operation by relieving the bandwidth-efficiency limit imposed on conventional single resonator systems. The design concept is the co-existence of a PT symmetry broken regime for a narrow-linewidth pump wave and near-exceptional point operation for broadband signal and idler waves. This enables us to achieve a new NOSP system with two orders of magnitude improvement in efficiency compared to a single resonator. With a highly nonlinear AlGaAs-on-Insulator platform, we demonstrate an NOSP at a data rate approaching 40 gigabits per second with a record low pump power of one milliwatt. These findings pave the way for the development of fully chip-scale NOSP devices with pump light sources integrated together, potentially leading to a wide range of applications in optical communication networks and classical or quantum computation. The combination of PT symmetry and NOSP may also open up opportunities for amplification, detection, and sensing, where response speed and efficiency are equally important. Supplementary Information: The online version contains supplementary material available at 10.1186/s43593-024-00062-w.

Memory/active T cell activation is associated with immunotherapeutic response in fumarate hydratase-deficient renal cell carcinoma.

PURPOSE: Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare and lethal subtype of kidney cancer. However, the optimal treatments and molecular correlates of benefits for FH-deficient RCC are currently lacking. EXPERIMENTAL DESIGN: A total of 91 patients with FH-deficient RCC from 15 medical centers between 2009 and 2022 were enrolled in this study. Genomic and bulk RNA sequencing (RNA-seq) were performed on 88 and 45 untreated FH-deficient RCCs, respectively. Single-cell RNA-seq was performed to identify biomarkers for treatment response. Main outcomes included disease-free survival (DFS) for localized patients, objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) for metastatic patients. RESULTS: In the localized setting, we found that a cell cycle progression signature enabled to predict disease progression. In the metastatic setting, first-line immune checkpoint inhibitor plus tyrosine kinase inhibitor (ICI+TKI) combination therapy showed satisfactory safety and was associated with a higher ORR (43.2% vs. 5.6%), apparently superior PFS (median PFS: 17.3 vs. 9.6 months, P=0.016) and OS (median OS: not reached vs. 25.7 months, P=0.005) over TKI monotherapy. Bulk and single-cell RNA-seq data revealed an enrichment of memory and effect T cells in responders to ICI plus TKI combination therapy. Furthermore, we identified a signature of memory and effect T cells that was associated with the effectiveness of ICI plus TKI combination therapy. CONCLUSIONS: ICI plus TKI combination therapy may represent a promising treatment option for metastatic FH-deficient RCC. A memory/active T cell-derived signature is associated with the efficacy of ICI+TKI but necessitates further validation.

Development and validation of a predictive model based on clinical and MpMRI findings to reduce additional systematic prostate biopsy.

OBJECTIVES: To develop and validate a predictive model based on clinical features and multiparametric magnetic resonance imaging (mpMRI) to reduce unnecessary systematic biopsies (SBs) in biopsy-naïve patients with suspected prostate cancer (PCa). METHODS: A total of 274 patients who underwent combined cognitive MRI-targeted biopsy (MRTB) with SB were retrospectively enrolled and temporally split into development (n = 201) and validation (n = 73) cohorts. Multivariable logistic regression analyses were used to determine independent predictors of clinically significant PCa (csPCa) on cognitive MRTB, and the clinical, MRI, and combined models were established respectively. Area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analyses were assessed. RESULTS: Prostate imaging data and reporting system (PI-RADS) score, index lesion (IL) on the peripheral zone, age, and prostate-specific antigen density (PSAD) were independent predictors and included in the combined model. The combined model achieved the best discrimination (AUC 0.88) as compared to both the MRI model incorporated by PI-RADS score, IL level, and zone (AUC 0.86) and the clinical model incorporated by age and PSAD (AUC 0.70). The combined model also showed good calibration and enabled great net benefit. Applying the combined model as a reference for performing MRTB alone with a cutoff of 60% would reduce 43.8% of additional SB, while missing 2.9% csPCa. CONCLUSIONS: The combined model based on clinical and mpMRI findings improved csPCa prediction and might be useful in making a decision about which patient could safely avoid unnecessary SB in addition to MRTB in biopsy-naïve patients. CRITICAL RELEVANCE STATEMENT: The combined model based on clinical and mpMRI findings improved csPCa prediction and might be useful in making a decision about which patient could safely avoid unnecessary SB in addition to MRTB in biopsy-naïve patients. KEY POINTS: • Age, PSAD, PI-RADS score, and peripheral index lesion were independent predictors of csPCa. • Risk models were used to predict the probability of detecting csPCa on cognitive MRTB. • The combined model might reduce 43.8% of unnecessary SBs, while missing 2.9% csPCa.

Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study.

Background: An increasing number of patients with suspected clinically significant prostate cancer (csPCa) are undergoing prostate multiparametric magnetic resonance imaging (mpMRI). The role of artificial intelligence (AI) algorithms in interpreting prostate mpMRI needs to be tested with multicenter external data. This study aimed to investigate the diagnostic efficacy of an AI model in detecting and localizing visible csPCa on mpMRI a multicenter external data set. Methods: The data of 2,105 patients suspected of having prostate cancer from four hospitals were retrospectively collected to develop an AI model to detect and localize suspicious csPCa. The lesions were annotated based on pathology records by two radiologists. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values were used as the input for the three-dimensional U-Net framework. Subsequently, the model was validated using an external data set comprising the data of 557 patients from three hospitals. Sensitivity, specificity, and accuracy were employed to evaluate the diagnostic efficacy of the model. Results: At the lesion level, the model had a sensitivity of 0.654. At the overall sextant level, the model had a sensitivity, specificity, and accuracy of 0.846, 0.884, and 0.874, respectively. At the patient level, the model had a sensitivity, specificity, and accuracy of 0.943, 0.776, and 0.849, respectively. The AI-predicted accuracy for the csPCa patients (231/245, 0.943) was significantly higher than that for the non-csPCa patients (242/312, 0.776) (P<0.001). The lesion number and tumor volume were greater in the correctly diagnosed patients than the incorrectly diagnosed patients (both P<0.001). Among the positive patients, those with lower average ADC values had a higher rate of correct diagnosis than those with higher average ADC values (P=0.01). Conclusions: The AI model exhibited acceptable accuracy in detecting and localizing visible csPCa at the patient and sextant levels. However, further improvements need to be made to enhance the sensitivity of the model at the lesion level.

Genomic and Evolutionary Characterization of Concurrent Intraductal Carcinoma and Adenocarcinoma of the Prostate.

Intraductal carcinoma of the prostate (IDC-P) is a lethal prostate cancer subtype that generally coexists with invasive high-grade prostate acinar adenocarcinoma (PAC) but exhibits distinct biological features compared with concomitant adenocarcinoma. In this study, we performed whole-exome, RNA, and DNA-methylation sequencing of IDC-P, concurrent invasive high-grade PAC lesions, and adjacent normal prostate tissues isolated from 22 radical prostatectomy specimens. Three evolutionary patterns of concurrent IDC-P and PAC were identified: early divergent, late divergent, and clonally distant. In contrast to those with a late divergent evolutionary pattern, tumors with clonally distant and early divergent evolutionary patterns showed higher genomic, epigenomic, transcriptional, and pathologic heterogeneity between IDC-P and PAC. Compared with coexisting PAC, IDC-P displayed increased expression of adverse prognosis-associated genes. Survival analysis based on an independent cohort of 505 patients with metastatic prostate cancer revealed that IDC-P carriers with lower risk International Society of Urological Pathology (ISUP) grade 1-4 adenocarcinoma displayed a castration-resistant free survival as poor as those with the highest risk ISUP grade 5 tumors that lacked concurrent IDC-P. Furthermore, IDC-P exhibited robust cell-cycle progression and androgen receptor activities, characterized by an enrichment of cellular proliferation-associated master regulators and genes involved in intratumoral androgen biosynthesis. Overall, this study provides a molecular groundwork for the aggressive behavior of IDC-P and could help identify potential strategies to improve treatment of IDC-P. SIGNIFICANCE: The genomic, transcriptomic, and epigenomic characterization of concurrent intraductal carcinoma and adenocarcinoma of the prostate deepens the biological understanding of this lethal disease and provides a genetic basis for developing targeted therapies.

Efficient and accurate numerical-projection of electromagnetic multipoles for scattering objects.

In this paper, we develop an efficient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical integration. Firstly, we briefly review the principles of multipole decomposition, highlighting two numerical projection methods including surface and volume integration. Secondly, we discuss the Lebedev and Gaussian quadrature methods, provide a detailed recipe to select the quadrature points and the corresponding weighting factor, and illustrate the integration accuracy and numerical efficiency (that is, with very few sampling points) using a unit sphere surface and regular tetrahedron. In the demonstrations of an isotropic dielectric nanosphere, a symmetric scatterer, and an anisotropic nanosphere, we perform multipole decomposition and validate our numerical projection procedure. The obtained results from our procedure are all consistent with those from Mie theory, symmetry constraints, and finite element simulations.

The efficacy of hydrogen/oxygen therapy favored the recovery of omicron SARS-CoV-2 variant infection: results of a multicenter, randomized, controlled trial.

Clinical studies had found that hydrogen/oxygen mixed inhalation was beneficial to ameliorate the respiratory symptoms in the adjuvant treatment of patients with COVID-19. We aimed to explore the efficacy of hydrogen/oxygen therapy in favoring the recovery of Omicron SARS-CoV-2 variant infection. There were 64 patients who randomly assigned to receive hydrogen/oxygen inhalation (32 patients) and oxygen inhalation (32 patients). The average shedding duration of Omicron in hydrogen/oxygen group was shorter than oxygen group. The trend of cumulative negative conversion rate of Omicron increased gradually after the third day. The IL-6 levels in hydrogen/oxygen group decreased by 22.8% compared with the baseline. After hydrogen/oxygen mixed gas inhalation, the lymphocyte count increased to 61.1% of the baseline on the 3rd day in the hydrogen/oxygen group. More patients in the hydrogen/oxygen group had resolution of pulmonary lesions. Our study showed the beneficial trends of molecular hydrogen in treating patients with COVID-19, which may offer a prospective solution to adjuvant therapy for COVID-19 Patients.

The Value of Radiological Imaging in Assessing Extrarenal Fat and Renal Vein Invasion in Renal Cell Carcinoma.

Renal cell carcinoma with extrarenal fat (perinephric or renal sinus fat) and renal vein invasion is the main evidence for the T3a stage according to the American Joint Committee on Cancer tumor-node-metastasis (TNM) staging system. Extrarenal fat invasion of renal cell carcinoma is defined as the presence of perinephric fat invasion or renal sinus fat invasion. Renal vein invasion is defined as the presence of main or segmental (branch) renal vein invasion. Accurate assessment of extrarenal fat and renal vein invasion is crucial for urologists to adopt the optimal therapeutic schedule, including radical nephrectomy or nephron-sparing treatments. Currently, imaging is still the most widely used means of examination for diagnosis and staging of renal cell carcinoma, especially multidetector computed tomography (MDCT). Therefore, we have, herein, summarized the latest progress and the future direction regarding imaging for assessing perinephric or renal sinus fat and renal vein invasion of renal cell carcinoma to assist clinical treatment selection and patient risk stratification.

Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study.

PURPOSE: To study the effect of artificial intelligence (AI) on the diagnostic performance of radiologists in interpreting prostate mpMRI images of the PI-RADS 3 category. METHODS: In this multicenter study, 16 radiologists were invited to interpret prostate mpMRI cases with and without AI. The study included a total of 87 cases initially diagnosed as PI-RADS 3 by radiologists without AI, with 28 cases being clinically significant cancers (csPCa) and 59 cases being non-csPCa. The study compared the diagnostic efficacy between readings without and with AI, the reading time, and confidence levels. RESULTS: AI changed the diagnosis in 65 out of 87 cases. Among the 59 non-csPCa cases, 41 were correctly downgraded to PI-RADS 1-2, and 9 were incorrectly upgraded to PI-RADS 4-5. For the 28 csPCa cases, 20 were correctly upgraded to PI-RADS 4-5, and 5 were incorrectly downgraded to PI-RADS 1-2. Radiologists assisted by AI achieved higher diagnostic specificity and accuracy than those without AI [0.695 vs 0.000 and 0.736 vs 0.322, both P < 0.001]. Sensitivity with AI was not significantly different from that without AI [0.821 vs 1.000, P = 1.000]. AI reduced reading time significantly compared to without AI (mean: 351 seconds, P < 0.001). The diagnostic confidence score with AI was significantly higher than that without AI (Cohen Kappa: -0.016). CONCLUSION: With the help of AI, there was an improvement in the diagnostic accuracy of PI-RADS category 3 cases by radiologists. There is also an increase in diagnostic efficiency and diagnostic confidence.

Multi-mode optical chirality extremizations on the incident momentum sphere.

We study the momentum-space evolutions for chiral optical responses of multi-mode resonators scattering plane waves of varying incident directions. It was revealed, in our previous study [Phys. Rev. Lett.126, 253901 (2021)10.1103/PhysRevLett.126.253901], that for single-mode resonators the scattering optical chiralities characterized by circular dichroism (CD) are solely decided by the third Stokes parameter distributions of the quasi-normal mode (QNM) radiations: CD = S3. Here we extend the investigations to multi-mode resonators, and explore numerically the dependence of optical chiralities on incident directions from the perspectives of QNM radiations and their circular polarization singularities. In contrast to the single-mode regime, for multi-mode resonators it is discovered that CDs defined in terms of extinction, scattering and absorption generally are different and cannot reach the ideal values of ±1 throughout the momentum sphere. Though the exact correspondence between CD and S3 does not hold anymore in the multi-mode regime, we demonstrate that the positions of the polarization singularities still serve as an efficient guide for identifying those incident directions where the optical chiralities can be extremized.

Genomic and transcriptomic features between primary and paired metastatic fumarate hydratase-deficient renal cell carcinoma.

BACKGROUND: Fumarate hydratase-deficient renal cell carcinoma (FH-RCC) is a rare highly aggressive subtype of kidney cancer for which the distinct genomic, transcriptomic, and evolutionary relationships between metastatic and primary lesions are still unclear. METHODS: In this study, whole-exome, RNA-seq, and DNA methylation sequencing were performed on primary-metastatic paired specimens from 19 FH-RCC cases, including 23 primary and 35 matched metastatic lesions. Phylogenetic and clonal evolutionary analyses were used to investigate the evolutionary characteristics of FH-RCC. Transcriptomic analyses, immunohistochemistry, and multiple immunofluorescence experiments were performed to identify the tumor microenvironmental features of metastatic lesions. RESULTS: Paired primary and metastatic lesions generally showed similar characteristics of tumor mutation burden, tumor neoantigen burden, microsatellite instability score, CNV burden, and genome instability index. Notably, we identified an FH-mutated founding MRCA (the most recent common ancestor) clone that dominated the early evolutionary trajectories in FH-RCC. Although both primary and metastatic lesions manifested high immunogenicity, metastatic lesions exhibited higher enrichment of T effector cells and immune-related chemokines, together with upregulation of PD-L1, TIGIT, and BTLA. In addition, we found that concurrent NF2 mutation may be associated with bone metastasis and upregulation of cell cycle signature in metastatic lesions. Furthermore, although in FH-RCC metastatic lesions in general shared similar CpG island methylator phenotype with primary lesions, we found metastatic lesions displaying hypomethylated chemokine and immune checkpoints related genomic loci. CONCLUSIONS: Overall, our study demonstrated the genomic, epigenomic, and transcriptomic features of metastatic lesions in FH-RCC and revealed their early evolutionary trajectory. These results provided multi-omics evidence portraying the progression of FH-RCC.

A diagnostic test of three-dimensional magnetic resonance elastography imaging for preoperative prediction of microvascular invasion in patients with T1 stage clear cell renal carcinoma.

Background: Detection of microvascular invasion (MVI) of kidney tumors is important for selecting the optimal therapeutic strategy. Currently, the prediction of MVI lacks an accurate imaging biomarker. This study evaluated the performance of three-dimensional (3D) magnetic resonance elastography (MRE) imaging in predicting microvascular invasion (MVI) of T1 stage clear cell renal carcinoma (ccRCC). Methods: In this prospective study, we conducted pre-surgical imaging with a clinical 3.0 T magnetic resonance imaging (MRI) system. Firstly, 83 consecutive patients were enrolled in this study. A 3D MRE stiffness map was generated and transferred to a post-processing workstation. Contrast-enhanced computed tomography (CT) was conducted to calculate the tumor enhancement ratio. The presence of MVI was evaluated by histopathological analysis and graded according to the risk stratification based upon the number and distribution. The mean stiffness and CT tumor enhancement ratio was calculated for tumors with or without MVI. The diagnostic performance [sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC)] and independent predicting factors for MVI were investigated. Results: Finally, A total of 80 patients (aged 46.7±13.2 years) were enrolled, including 22 cases of tumors with MVI. The mean MRE stiffness of kidney parenchyma and kidney tumors was 4.8±0.2 and 4.5±0.7 kPa, respectively. There was significant difference in the mean MRE stiffness between tumors with MVI (5.4±0.6 kPa) and tumors without MVI (4.1±0.3 kPa) (P<0.05). The sensitivity, specificity, positive predictive value, negative predictive value, and the AUC for mean stiffness in the prediction of MVI were 100%, 75%, 63%, 96%, and 0.87 [95% confidence interval (CI): 0.72, 0.94], respectively. The corresponding values for the CT tumor enhancement ratio were 90%, 80%, 63%, 96%, and 0.88 (95% CI: 0.71, 0.93), respectively. The odds ratio (OR) value for MRE tumor stiffness and CT kidney tumor enhancement ratio in the prediction of MVI was 2.9 (95% CI: 1.8, 3.7) and 1.2 (95% CI: 1.0, 1.7), respectively (P>0.05). Conclusions: 3D MRE imaging has promising diagnostic performance for predicting MVI in T1 stage ccRCC, which may improve the reliability of surgical strategy selection with T1 stage ccRCC.

TaG-Net: Topology-Aware Graph Network for Centerline-Based Vessel Labeling.

Anatomical labeling of head and neck vessels is a vital step for cerebrovascular disease diagnosis. However, it remains challenging to automatically and accurately label vessels in computed tomography angiography (CTA) since head and neck vessels are tortuous, branched, and often spatially close to nearby vasculature. To address these challenges, we propose a novel topology-aware graph network (TaG-Net) for vessel labeling. It combines the advantages of volumetric image segmentation in the voxel space and centerline labeling in the line space, wherein the voxel space provides detailed local appearance information, and line space offers high-level anatomical and topological information of vessels through the vascular graph constructed from centerlines. First, we extract centerlines from the initial vessel segmentation and construct a vascular graph from them. Then, we conduct vascular graph labeling using TaG-Net, in which techniques of topology-preserving sampling, topology-aware feature grouping, and multi-scale vascular graph are designed. After that, the labeled vascular graph is utilized to improve volumetric segmentation via vessel completion. Finally, the head and neck vessels of 18 segments are labeled by assigning centerline labels to the refined segmentation. We have conducted experiments on CTA images of 401 subjects, and experimental results show superior vessel segmentation and labeling of our method compared to other state-of-the-art methods.

A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI.

BACKGROUND: AI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software. MATERIALS AND METHODS: In total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed. RESULTS: On lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p < .001). On patient level, AI-aided improved the specificity from 57.7 to 71.7% (14.0% increase, 95% CI [6.4, 21.4]; p < .001) while preserving the sensitivity (88.3% vs. 93.9%, p = 0.06). AI-aided reduced the median reading time of one case by 56.3% from 423 to 185 s (238-s decrease, 95% CI [219, 260]; p < .001), and the median diagnostic confidence score was increased by 10.3% from 3.9 to 4.3 (0.4-score increase, 95% CI [0.3, 0.5]; p < .001). CONCLUSIONS: AI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients and also improving reading times and diagnostic confidence. CLINICAL RELEVANCE STATEMENT: This study involves the process of data collection, randomization and crossover reading procedure.

Neuroendocrine differentiation predicts the therapeutic efficacy of abiraterone and docetaxel as first-line therapy in metastatic castration-resistant prostate cancer.

PURPOSE: We aim to explore the predictive value of neuroendocrine differentiation (NED) in patients with metastatic castration-resistant prostate cancer (mCRPC) receiving abiraterone or docetaxel as first-line therapy. METHODS: We retrospectively analyzed data of 262 mCRPC patients receiving abiraterone or docetaxel as first-line mCRPC treatment. NED was evaluated using prostate biopsy samples at the time of mCRPC by immunohistochemical staining. Kaplan-Meier curves and Cox regression were used to assess the association between NED and treatment outcomes including PSA progression-free survival (PSA-PFS), radiographic progression-free survival (rPFS), and overall survival (OS). RESULTS: NED was confirmed in 100/262 (38.2%) mCRPC patients, with 76/100 (76.0%) and 24/100 (24.0%) men harboring NED < 10% and NED ≥ 10%, respectively. 203/262 (77.5%) and 59/262 (22.5%) patients received abiraterone and docetaxel, respectively. In abiraterone treatment, NED was associated with a significantly shorter median PSA-PFS (mPSA-PFS, 7.5 vs. 10.3-Mo, P < 0.001), median rPFS (mrPFS, 15.9 vs. 19.5-Mo, P = 0.010), and median OS (mOS, 23.2 vs. 34.3-Mo, P = 0.014)). Likewise, for mCRPC patients receiving docetaxel, the positive detection of NED also predicted shorter mPSA-PFS (3.8 vs. 5.9-Mo, P = 0.052), mrPFS (8.4 vs. 20.4-Mo, P = 0.016) and mOS (13.6 vs. 29.0-Mo, P = 0.033). The adverse prognostic trait of NED is consistent in most subgroups. Additionally, patients' survival outcomes deteriorated as the NED proportion grew in both therapies. After propensity score matching, NED-positive patients showed comparable prognosis in abiraterone and docetaxel therapy. CONCLUSION: For mCRPC patients receiving abiraterone or docetaxel, NED and its proportion were critical predictive factors. NED detection at mCRPC might aid in predicting patients' outcomes and optimizing treatment decisions.

Quality of radiomics for predicting microvascular invasion in hepatocellular carcinoma: a systematic review.

OBJECTIVES: To comprehensively evaluate the reporting quality, risk of bias, and radiomics methodology quality of radiomics models for predicting microvascular invasion in hepatocellular carcinoma. METHODS: A systematic search of available literature was performed in PubMed, Embase, Web of Science, Scopus, and the Cochrane Library up to January 21, 2022. Studies that developed and/or validated machine learning models based on radiomics data to predict microvascular invasion in hepatocellular carcinoma were included. These studies were reviewed by two investigators and the consensus data were used for analyzing. The reporting quality, risk of bias, and radiomics methodological quality were evaluated by Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD), Prediction model Risk of Bias Assessment Tool, and Radiomics Quality Score (RQS), respectively. RESULTS: A total of 30 studies met eligibility criteria with 24 model developing studies and 6 model developing and external validation studies. The median overall TRIPOD adherence was 75.4% (range 56.7-94.3%). All studies were at high risk of bias with at least 2 of 20 sources of bias. Furthermore, 28 studies showed unclear risks of bias in up to 5 signaling questions because of the lack of specified reports. The median RQS score was 37.5% (range 25-61.1%). CONCLUSION: Current radiomic models for MVI-status prediction have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. KEY POINTS: • Current microvascular invasion prediction radiomics studies have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. • Data representativeness, feature robustness, events-per-variable ratio, evaluation metrics, and appropriate validation are five main aspects futures studies should focus more on to improve the quality of radiomics. • Both Radiomics Quality Score and Prediction model Risk of Bias Assessment Tool are needed to comprehensively evaluate a radiomics study.

Delta Radiomics Model Predicts Lesion-Level Responses to Tyrosine Kinase Inhibitors in Patients with Advanced Renal Cell Carcinoma: A Preliminary Result.

BACKGROUND: This study aimed to develop and internally validate computed tomography (CT)-based radiomic models to predict the lesion-level short-term response to tyrosine kinase inhibitors (TKIs) in patients with advanced renal cell carcinoma (RCC). METHODS: This retrospective study included consecutive patients with RCC that were treated using TKIs as the first-line treatment. Radiomic features were extracted from noncontrast (NC) and arterial-phase (AP) CT images. The model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 36 patients with 131 measurable lesions were enrolled (training: validation = 91: 40). The model with five delta features achieved the best discrimination capability with AUC values of 0.940 (95% CI, 0.890‒0.990) in the training cohort and 0.916 (95% CI, 0.828‒1.000) in the validation cohort. Only the delta model was well calibrated. The DCA showed that the net benefit of the delta model was greater than that of the other radiomic models, as well as that of the treat-all and treat-none criteria. CONCLUSIONS: Models based on CT delta radiomic features may help predict the short-term response to TKIs in patients with advanced RCC and aid in lesion stratification for potential treatments.

Avoiding Unnecessary Systematic Biopsy in Clinically Significant Prostate Cancer: Comparison Between MRI-Based Radiomics Model and PI-RADS Category.

BACKGROUND: MRI-targeted biopsy (MRTB) improves the clinically significant prostate cancer (csPCa) detection rate with fewer biopsy cores in men with suspected PCa. However, whether concurrent systematic biopsy (SB) can be avoided in patients undergoing MRTB remains unclear. PURPOSE: To evaluate the potential value of MRI-based radiomics models in avoiding unnecessary SB in biopsy-naïve patients. STUDY TYPE: Retrospective. POPULATION: A total of 226 patients (mean age 66.6 ± 9.02 years) with suspicion of PCa (PI-RADS score ≥ 3) and received combined cognitive MRTB with SB were retrospectively recruited and randomly divided into training (N = 180) and test (N = 46) cohorts at an 8:2 ratio. FIELD STRENGTH/SEQUENCE: A 3.0 T, biparametric MRI (bpMRI) including T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map. ASSESSMENT: The whole prostate gland (PG) and the index lesion (IL) were delineated. Three radiomics models of bpMRIPG , bpMRIIL , and bpMRIPG+IL were constructed, respectively, and the performance of each radiomics model was compared with that of PI-RADS assessment. STATISTICAL TESTS: The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. The area under the curve (AUC) and decision curve analysis were used to estimate the models. RESULTS: The bpMRIPG+IL radiomics model exhibited good discrimination, calibration, and net benefits, which would reduce the SB biopsy in 71.2% and 71.4% of men with PI-RADS ≥ 5 lesions in the training and test cohorts, respectively. DATA CONCLUSION: A bpMRIPG+IL radiomics model may outperform PI-RADS category in help reducing unnecessary SB in biopsy-naïve patients. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 6.