Compressed sensing 3D T2WI radiomics model: improving diagnostic performance in muscle invasion of bladder cancer.

BACKGROUND: Preoperative discrimination between non-muscle-invasive bladder cancer (NMIBC) and the muscle invasive bladder cancer (MIBC) is a determinant of management. The purpose of this research is to employ radiomics to evaluate the diagnostic value in determining muscle invasiveness of compressed sensing (CS) accelerated 3D T2-weighted-SPACE sequence with high resolution and short acquisition time. METHODS: This prospective study involved 108 participants who underwent preoperative 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted sequences. The cohort was divided into training and validation cohorts in a 7:3 ratio. In the training cohort, a Rad-score was constructed based on radiomic features selected by intraclass correlation coefficients, pearson correlation coefficient and least absolute shrinkage and selection operator . Multivariate logistic regression was used to develop a nomogram combined radiomics and clinical indices. In the validation cohort, the performances of the models were evaluated by ROC, calibration, and decision curves. RESULTS: In the validation cohort, the area under ROC curve of 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted models were 0.87(95% confidence interval (CI):0.73-1.00), 0.79(95%CI:0.63-0.96) and 0.77(95%CI:0.60-0.93), respectively. The differences in signal-to-noise ratio and contrast-to-noise ratio between 3D-CS-T2-weighted-SPACE and 3D-T2-weighted-SPACE sequences were not statistically significant(p > 0.05). While the clinical model composed of three clinical indices was 0.74(95%CI:0.55-0.94) and the radiomics-clinical nomogram model was 0.88(95%CI:0.75-1.00). The calibration curves confirmed high goodness of fit, and the decision curve also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model. CONCLUSION: The radiomics model based on compressed sensing 3D T2WI sequence, which was acquired within a shorter acquisition time, showed superior diagnostic efficacy in muscle invasion of bladder cancer. Additionally, the nomogram model could enhance the diagnostic performance.

Non-Gaussian diffusion metrics with whole-tumor histogram analysis for bladder cancer diagnosis: muscle invasion and histological grade.

PURPOSE: To investigate the performance of histogram features of non-Gaussian diffusion metrics for diagnosing muscle invasion and histological grade in bladder cancer (BCa). METHODS: Patients were prospectively allocated to MR scanner1 (training cohort) or MR2 (testing cohort) for conventional diffusion-weighted imaging (DWIconv) and multi-b-value DWI. Metrics of continuous time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) were simultaneously calculated using multi-b-value DWI. Whole-tumor histogram features were extracted from DWIconv and non-Gaussian diffusion metrics for logistic regression analysis to develop diffusion models diagnosing muscle invasion and histological grade. The models' performances were quantified by area under the receiver operating characteristic curve (AUC). RESULTS: MR1 included 267 pathologically-confirmed BCa patients (median age, 67 years [IQR, 46-82], 222 men) and MR2 included 83 (median age, 65 years [IQR, 31-82], 73 men). For discriminating muscle invasion, CTRW achieved the highest testing AUC of 0.915, higher than DWIconv's 0.805 (p = 0.014), and similar to the combined diffusion model's AUC of 0.885 (p = 0.076). For differentiating histological grade of non-muscle-invasion bladder cancer, IVIM outperformed a testing AUC of 0.897, higher than DWIconv's 0.694 (p = 0.020), and similar to the combined diffusion model's AUC of 0.917 (p = 0.650). In both tasks, DKI, FROC, and SEM failed to show diagnostic superiority over DWIconv (p > 0.05). CONCLUSION: CTRW and IVIM are two potential non-Gaussian diffusion models to improve the MRI application in assessing muscle invasion and histological grade of BCa, respectively. CRITICAL RELEVANCE STATEMENT: Our study validates non-Gaussian diffusion imaging as a reliable, non-invasive technique for early assessment of muscle invasion and histological grade in BCa, enhancing accuracy in diagnosis and improving MRI application in BCa diagnostic procedures. KEY POINTS: Muscular invasion largely determines bladder salvageability in bladder cancer patients. Evaluated non-Gaussian diffusion metrics surpassed DWIconv in BCa muscle invasion and histological grade diagnosis. Non-Gaussian diffusion imaging improved MRI application in preoperative diagnosis of BCa.

Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics.

OBJECTIVE: We aimed to develop a radiomics-clinical nomogram using multi-sequence MRI to predict recurrence-free survival (RFS) in bladder cancer (BCa) patients and assess its superiority over clinical models. METHODS: A retrospective cohort of 229 BCa patients with preoperative multi-sequence MRI was divided into a training set (n = 160) and a validation set (n = 69). Radiomics features were extracted from T2-weighted images, diffusion-weighted imaging, apparent diffusion coefficient, and dynamic contrast-enhanced images. Effective features were identified using the least absolute shrinkage and selection operator (LASSO) method. Clinical risk factors were determined via univariate and multivariate Cox analysis, leading to the creation of a radiomics-clinical nomogram. Kaplan-Meier analysis and log-rank tests assessed the relationship between radiomics features and RFS. We calculated the net reclassification improvement (NRI) to evaluate the added value of the radiomics signature and used decision curve analysis (DCA) to assess the nomogram's clinical validity. RESULTS: Radiomics features significantly correlated with RFS (log-rank p < 0.001) and were independent of clinical factors (p < 0.001). The combined model, incorporating radiomics features and clinical data, demonstrated the best prognostic value, with C-index values of 0.853 in the training set and 0.832 in the validation set. Compared to the clinical model, the radiomics-clinical nomogram exhibited superior calibration and classification (NRI: 0.6768, 95% CI: 0.5549-0.7987, p < 0.001). CONCLUSION: The radiomics-clinical nomogram, based on multi-sequence MRI, effectively assesses the BCa recurrence risk. It outperforms both the radiomics model and the clinical model in predicting BCa recurrence risk. CRITICAL RELEVANCE STATEMENT: The radiomics-clinical nomogram, utilizing multi-sequence MRI, holds promise for predicting bladder cancer recurrence, enhancing individualized clinical treatment, and performing tumor surveillance. KEY POINTS: • Radiomics plays a vital role in predicting bladder cancer recurrence. • Precise prediction of tumor recurrence risk is crucial for clinical management. • MRI-based radiomics models excel in predicting bladder cancer recurrence.

MRI radiomics for predicting poor disease-free survival in muscle invasive bladder cancer: the results of the retrospective cohort study.

OBJECTIVES: To develop an MRI radiomic nomogram capable of identifying muscle invasive bladder cancer (MIBC) patients with high-risk molecular characteristics related to poor 2-year disease-free survival (DFS). METHODS: We performed a retrospective analysis of DNA sequencing data, prognostic information, and radiomics features from 91 MIBC patients at stages T2-T4aN0M0 without history of immunotherapy. To identify risk stratification, we employed Cox regression based on TP53 mutation status and tumor mutational burden (TMB) level. Radiomics signatures were selected using the least absolute shrinkage and selection operator (LASSO) to construct a nomogram based on logistic regression for predicting the stratification in the training cohort. The predictive performance of the nomogram was assessed in the testing cohort using receiver operator curve (ROC), Hosmer-Lemeshow (HL) test, clinical impact curve (CIC), and decision curve analysis (DCA). RESULTS: Among 91 participants, the mean TMB value was 3.3 mut/Mb, with 60 participants having TP53 mutations. Patients with TP53 mutations and a below-average TMB value were identified as high risk and had a significantly poor 2-year DFS (hazard ratio = 4.36, 95% CI 1.82-10.44, P < 0.001). LASSO identified five radiomics signatures that correlated with the risk stratification. In the testing cohort, the nomogram achieved an area under the ROC curve of 0.909 (95% CI 0.789-0.991) and an accuracy of 0.889 (95% CI 0.708-0.977). CONCLUSION: The molecular risk stratification based on TP53 mutation status combined with TMB level is strongly associated with DFS in MIBC. Radiomics signatures can effectively predict this stratification and provide valuable information to clinical decision-making.

Synthetic MRI in breast cancer: differentiating benign from malignant lesions and predicting immunohistochemical expression status.

To evaluate and compare the performance of synthetic magnetic resonance imaging (SyMRI) in classifying benign and malignant breast lesions and predicting the expression status of immunohistochemistry (IHC) markers. We retrospectively analysed 121 patients with breast lesions who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and SyMRI before surgery in our hospital. DCE-MRI was used to assess the lesions, and then regions of interest (ROIs) were outlined on SyMRI (before and after enhancement), and apparent diffusion coefficient (ADC) maps to obtain quantitative values. After being grouped according to benign and malignant status, the malignant lesions were divided into high and low expression groups according to the expression status of IHC markers. Logistic regression was used to analyse the differences in independent variables between groups. The performance of the modalities in classification and prediction was evaluated by receiver operating characteristic (ROC) curves. In total, 57 of 121 lesions were benign, the other 64 were malignant, and 56 malignant lesions performed immunohistochemical staining. Quantitative values from proton density-weighted imaging prior to an injection of the contrast agent (PD-Pre) and T2-weighted imaging (T2WI) after the injection (T2-Gd), as well as its standard deviation (SD of T2-Gd), were valuable SyMRI parameters for the classification of benign and malignant breast lesions, but the performance of SyMRI (area under the curve, AUC = 0.716) was not as good as that of ADC values (AUC = 0.853). However, ADC values could not predict the expression status of breast cancer markers, for which SyMRI had excellent performance. The AUCs of androgen receptor (AR), estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), p53 and Ki-67 were 0.687, 0.890, 0.852, 0.746, 0.813 and 0.774, respectively. SyMRI had certain value in distinguishing between benign and malignant breast lesions, and ADC values were still the ideal method. However, to predict the expression status of IHC markers, SyMRI had an incomparable value compared with ADC values.

MRI radiomics-based evaluation of tuberculous and brucella spondylitis.

OBJECTIVES: We analyzed magnetic resonance imaging (MRI) and radiomics labels from tuberculous spondylitis (TBS) and brucella spondylitis (BS) to build machine learning models that differentiate TBS from BS and culture-positive TBS (TBS(+)) from culture-negative TBS (TBS(-). METHODS: This retrospective study included 56 patients with BS, 63 patients with TBS(+) and 71 patients with TBS(-). Radiomics labels were extracted from T2-weighted fat-suppression images. MRI labels were analyzed via logistic regression (LR); radiomics labels were analyzed by t-tests, SelectKBest, and least absolute shrinkage and selection operator (LASSO). Random forest (RF) and support vector machine (SVM) models were established using radiomics or joint (radiomics+MRI) labels. Models were evaluated by receiver operating characteristic curves, areas under the curve (AUCs), decision curve analysis (DCA), and Hosmer-Lemeshow tests. RESULTS: When joint-label models were used to compare BS vs TBS(+) and BS vs TBS(-) groups, SVM AUCs were 0.904 and 0.944, respectively, whereas RF AUCs were 0.950 and 0.947, respectively; these were higher than the AUCs of the MRI label-based LR model. DCA showed that radiomics-based machine learning models had a greater net benefit; Hosmer-Lemeshow tests demonstrated good prediction consistency for all models. CONCLUSIONS: Radiomics can help distinguish TBS from BS and TBS(+) from TBS(-).

Integrated Full-Length Transcriptome and RNA-Seq to Identify Immune System Genes from the Skin of Sperm Whale (Physeter macrocephalus).

Cetaceans are a group of secondary aquatic mammals whose ancestors returned to the ocean from land, and during evolution, their immune systems adapted to the aquatic environment. Their skin, as the primary barrier to environmental pathogens, supposedly evolved to adapt to a new living environment. However, the immune system in the skin of cetaceans and the associated molecular mechanisms are still largely unknown. To better understand the immune system, we extracted RNA from the sperm whale's (Physeter macrocephalus) skin and performed PacBio full-length sequencing and RNA-seq sequencing. We obtained a total of 96,350 full-length transcripts with an average length of 1705 bp and detected 5150 genes that were associated with 21 immune-related pathways by gene annotation enrichment analysis. Moreover, we found 89 encoding genes corresponding to 33 proteins were annotated in the NOD-like receptor (NLR)-signaling pathway, including NOD1, NOD2, RIP2, and NF-kB genes, which were discussed in detail and predicted to play essential roles in the immune system of the sperm whale. Furthermore, NOD1 was highly conservative during evolution by the sequence comparison and phylogenetic tree. These results provide new information about the immune system in the skin of cetaceans, as well as the evolution of immune-related genes.

Evaluation of the Peripheral Rim Instability of the Discoid Meniscus in Children by Using Weight-Bearing Magnetic Resonance Imaging.

OBJECTIVE: The aim of the study was to assess the peripheral rim instability and the clinical value of discoid meniscus. METHODS: We retrospectively studied 79 magnetic resonance imaging (MRI) examinations of discoid meniscus from May 2017 to September 2019. The patient symptoms and physical findings were documented. The patients underwent "dedicated" 0.25 T supine and weight-bearing MRI examination. Finally, all patients underwent arthroscopy. RESULTS: Sound/clicking during motion (P = 0.009) and limited extension (P = 0.044) of subjective symptoms, clunk during motion (P = 0.035), and flexion contracture (P = 0.012) of physical findings were significant predictors of peripheral rim instability. The comparison of the weight-bearing MRI with the supine position MRI demonstrated that the disformed discoid meniscus was shifted significantly and that no shift was displaced centrally (P = 0.001). A correlation between discoid meniscal displacement and the presence of peripheral rim instability in arthroscopy was noted (P < 0.001) using weight-bearing MRI. CONCLUSIONS: The clinical symptoms of the patients combined with weight-bearing MRI can determine peripheral rim instability optimally.