Investigation of clear cell renal cell carcinoma grades using diffusion-relaxation correlation spectroscopic imaging with optimized spatial-spectrum analysis.

OBJECTIVES: To differentiate high-grade from low-grade clear cell renal cell carcinoma (ccRCC) using diffusion-relaxation correlation spectroscopic imaging (DR-CSI) spectra in an equal separating analysis. METHODS: Eighty patients with 86 pathologically confirmed ccRCCs who underwent DR-CSI were enrolled. Two radiologists delineated the region of interest. The spectrum was derived based on DR-CSI and was further segmented into multiple equal subregions from 2*2 to 9*9. The agreement between the 2 radiologists was assessed by the intraclass correlation coefficient (ICC). Logistic regression was used to establish the regression model for differentiation, and 5-fold cross-validation was used to evaluate its accuracy. McNemar's test was used to compare the diagnostic performance between equipartition models and the traditional parameters, including the apparent diffusion coefficient (ADC) and T2 value. RESULTS: The inter-reader agreement decreased as the divisions in the equipartition model increased (overall ICC ranged from 0.859 to 0.920). The accuracy increased from the 2*2 to 9*9 equipartition model (0.68 for 2*2, 0.69 for 3*3 and 4*4, 0.70 for 5*5, 0.71 for 6*6, 0.78 for 7*7, and 0.75 for 8*8 and 9*9). The equipartition models with divisions >7*7 were significantly better than ADC and T2 (vs ADC: P = .002-.008; vs T2: P = .001-.004). CONCLUSIONS: The equipartition method has the potential to analyse the DR-CSI spectrum and discriminate between low-grade and high-grade ccRCC. ADVANCES IN KNOWLEDGE: The evaluation of DR-CSI relies on prior knowledge, and how to assess the spectrum derived from DR-CSI without prior knowledge has not been well studied.

The added value of AI-based computer-aided diagnosis in classification of cancer at prostate MRI.

OBJECTIVES: To develop an artificial intelligence (AI) model for prostate segmentation and prostate cancer (PCa) detection, and explore the added value of AI-based computer-aided diagnosis (CAD) compared to conventional PI-RADS assessment. METHODS: A retrospective study was performed on multi-centers and included patients who underwent prostate biopsies and multiparametric MRI. A convolutional-neural-network-based AI model was trained and validated; the reliability of different CAD methods (concurrent read and AI-first read) were tested in an internal/external cohort. The diagnostic performance, consistency and efficiency of radiologists and AI-based CAD were compared. RESULTS: The training/validation/internal test sets included 650 (400/100/150) cases from one center; the external test included 100 cases (25/25/50) from three centers. For diagnosis accuracy, AI-based CAD methods showed no significant differences and were equivalent to the radiologists in the internal test (127/150 vs. 130/150 vs. 125/150 for reader 1; 127/150 vs.132/150 vs. 131/150 for reader 2; all p > 0.05), whereas in the external test, concurrent-read methods were superior/equal to AI-first read (87/100 vs. 71/100, p < 0.001, for reader 2; 79/100 vs. 69/100, p = 0.076, for reader 1) and better than/equal to radiologists (79/100 vs. 72/100, p = 0.039, for reader 1; 87/100 vs. 86/100, p = 1.000, for reader 2). Moreover, AI-first read/concurrent read improved consistency in both internal test (κ = 1.000, 0.830) and external test (κ = 0.958, 0.713) compared to radiologists (κ = 0.747, 0.600); AI-first read method (8.54 s/7.66 s) was faster than readers (92.72 s/89.54 s) and concurrent-read method (29.15 s/28.92 s), respectively. CONCLUSION: AI-based CAD could improve the consistency and efficiency for accurate diagnosis; the concurrent-read method could enhance the diagnostic capabilities of an inexperienced radiologist in unfamiliar situations. KEY POINTS: • For prostate cancer segmentation, the performance of multi-small Vnet displays optimal compared to small Vnet and Vnet (DSCmsvnet vs. DSCsvnet, p = 0.021; DSCmsvnet vs. DSCvnet, p < 0.001). • For prostate gland segmentation, the mean/median DSCs for fine and coarse segmentation were 0.91/0.91 and 0.88/0.89, respectively. Fine segmentation displays superior performance compared to coarse (DSCcoarse vs. DSCfine, p < 0.001). • For PCa diagnosis, AI-based CAD methods improve consistency in internal (κ = 1.000; 0.830) and external (κ = 0.958; 0.713) tests compared to radiologists (κ = 0.747; 0.600); the AI-first read (8.54 s/7.66 s) was faster than the readers (92.72 s/89.54 s) and the concurrent-read method (29.15 s/28.92 s).

The Diagnostic Value of PI-RADS v2.1 in Patients with a History of Transurethral Resection of the Prostate (TURP).

To explore the diagnostic value of the Prostate Imaging−Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CSPCa) in patients with a history of transurethral resection of the prostate (TURP), we conducted a retrospective study of 102 patients who underwent systematic prostate biopsies with TURP history. ROC analyses and logistic regression analyses were performed to demonstrate the diagnostic value of PI-RADS v2.1 and other clinical characteristics, including PSA and free/total PSA (F/T PSA). Of 102 patients, 43 were diagnosed with CSPCa. In ROC analysis, PSA, F/T PSA, and PI-RADS v2.1 demonstrated significant diagnostic value in detecting CSPCa in our cohort (AUC 0.710 (95%CI 0.608−0.812), AUC 0.768 (95%CI 0.676−0.860), AUC 0.777 (95%CI 0.688−0.867), respectively). Further, PI-RADS v2.1 scores of the peripheral and transitional zones were analyzed separately. In ROC analysis, PI-RADS v2.1 remained valuable in identifying peripheral-zone CSPCa (AUC 0.780 (95%CI 0.665−0.854; p < 0.001)) while having limited capability in distinguishing transitional zone lesions (AUC 0.533 (95%CI 0.410−0.557; p = 0.594)). PSA and F/T PSA retain significant diagnostic value for CSPCa in patients with TURP history. PI-RADS v2.1 is reliable for detecting peripheral-zone CSPCa but has limited diagnostic value when assessing transitional zone lesions.

MR Spectroscopy for Detecting Fumarate Hydratase Deficiency in Hereditary Leiomyomatosis and Renal Cell Carcinoma Syndrome.

Background Noninvasive in vivo detection of fumarate accumulation may help identify fumarate hydratase deficiency in renal cancer related to hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome. Purpose To investigate the feasibility of MR spectroscopy (MRS) in detecting elevated fumarate levels in HLRCC-associated renal cancers. Materials and Methods This study included an experimental xenograft mouse model and prospective clinical cohort. First, MRS was performed on patient-derived tumor xenograft models and control models to detect fumarate. Then, consecutive participants with clinical suspicion of HLRCC-associated renal tumors were enrolled. For the detection of fumarate, MRS results were classified as detected, borderline, undetected, or technical failure. The sensitivity, specificity, and accuracy of MRS for diagnosing HLRCC-associated renal cancer were assessed. The signal-to-noise ratio (SNR) of the fumarate peak was calculated and evaluated with receiver operating characteristic curve analysis. Results Fumarate peaks were detected at 6.54 parts per million in all three patient-derived xenograft models. A total of 38 participants (21 men; mean age, 47 years [range, 18-71 years]) with 46 lesions were analyzed. All primary HLRCC-associated renal cancers showed a fumarate peak; among the seven metastatic HLRCC-associated lesions, a fumarate peak was detected in three lesions and borderline in two. When only detected peaks were regarded as positive findings, the sensitivity, specificity, and accuracy of MRS at the lesion level were 69% (nine of 13 lesions), 100% (33 of 33 lesions), and 91% (42 of 46 lesions), respectively. When borderline peaks were also included as a positive finding, the sensitivity, specificity, and accuracy reached 85% (11 of 13 lesions), 88% (29 of 33 lesions), and 87% (40 of 46 lesions), respectively. The SNR of fumarate showed an area under the receiver operating characteristic curve of 0.87 for classifying HLRCC-associated tumors. Conclusion MR spectroscopy of fumarate was sensitive and specific for hereditary leiomyomatosis and renal cell carcinoma-associated tumors. © RSNA, 2022 Online supplemental material is available for this article.

A critical review on the technique and mechanism of microwave-based denitrification in flue gas.

Microwave radiation has received extensive attention due to its significant thermal and non-thermal effects, and the development of MW-based denitrification in flue gas has become one of the most promising methods to avoid the defects of ammonia escape, high temperature and cost in traditional SCR. This review introduces the thermal and non-thermal effects of microwaves and divides MW-based denitrification methods into MW reduction and oxidation denitrification, systematically summarizes these denitrification methods, including MW discharge reduction, MW-induced catalytic reduction using active carbon, molecular sieves, metal oxides (transition metals, perovskites, etc.), MW-induced oxidation denitrification with and without additional oxidant, and discusses their removal pathway and mechanism. Finally, several research prospects and directions regarding the development of microwave-based denitrification methods are provided.