The influence of Gleason score ≤ 6 histology on the outcome of high-risk localized prostate cancer after modern radiotherapy.

We aimed to retrospectively review outcomes in patients with high-risk prostate cancer and a Gleason score ≤ 6 following modern radiotherapy. We analyzed the outcomes of 1374 patients who had undergone modern radiotherapy, comprising a high-risk low grade [HRLG] group (Gleason score ≤ 6; n = 94) and a high-risk high grade [HRHG] group (Gleason score ≥ 7, n = 1125). We included 955 patients who received brachytherapy with or without external beam radio-therapy (EBRT) and 264 who received modern EBRT (intensity-modulated radiotherapy [IMRT] or stereotactic body radiotherapy [SBRT]). At a median follow-up of 60 (2-177) months, actuarial 5-year biochemical failure-free survival rates were 97.8 and 91.8% (p = 0.017), respectively. The frequency of clinical failure in the HRLG group was less than that in the HRHG group (0% vs 5.4%, p = 0.012). The HRLG group had a better 5-year distant metastasis-free survival than the HRHG group (100% vs 96.0%, p = 0.035). As the HRLG group exhibited no clinical failure and better outcomes than the HRHG group, the HRLG group might potentially be classified as a lower-risk group.

Expected impact of MRI-targeted biopsy interreader variability among uropathologists on ProScreen prostate cancer screening trial: a pre-trial validation study.

PURPOSE: Prostate cancer (PCa) histology, particularly the Gleason score, is an independent prognostic predictor in PCa. Little is known about the inter-reader variability in grading of targeted prostate biopsy based on magnetic resonance imaging (MRI). The aim of this study was to assess inter-reader variability in Gleason grading of MRI-targeted biopsy among uropathologists and its potential impact on a population-based randomized PCa screening trial (ProScreen). METHODS: From June 2014 to May 2018, 100 men with clinically suspected PCa were retrospectively selected. All men underwent prostate MRI and 86 underwent targeted prostate of the prostate. Six pathologists individually reviewed the pathology slides of the prostate biopsies. The five-tier ISUP (The International Society of Urological Pathology) grade grouping (GG) system was used. Fleiss' weighted kappa (κ) and Model-based kappa for associations were computed to estimate the combined agreement between individual pathologists. RESULTS: GG reporting of targeted prostate was highly consistent among the trial pathologists. Inter-reader agreement for cancer (GG1-5) vs. benign was excellent (Model-based kappa 0.90, Fleiss' kappa κ = 0.90) and for clinically significant prostate cancer (csPCa) (GG2-5 vs. GG0 vs. GG1), it was good (Model-based kappa 0.70, Fleiss' kappa κ 0.67). CONCLUSIONS: Inter-reader agreement in grading of MRI-targeted biopsy was good to excellent, while it was fair to moderate for MRI in the same cohort, as previously shown. Importantly, there was wide consensus by pathologists in assigning the contemporary GG on MRI-targeted biopsy suggesting high reproducibility of pathology reporting in the ProScreen trial.

Assessment of the accuracy of biparametric MRI/TRUS fusion-guided biopsy for index tumor evaluation using postoperative pathology specimens.

BACKGROUND: Multiparametric MRI (mpMRI) is widely used for the diagnosis, surveillance, and staging of prostate cancer. However, it has several limitations, including higher costs, longer examination times, and the use of gadolinium-based contrast agents. This study aimed to investigate the accuracy of preoperatively assessed index tumors (ITs) using biparametric MRI (bpMRI)/transrectal ultrasound (TRUS) fusion biopsy compared with radical prostatectomy (RP) specimens. METHODS: We included 113 patients diagnosed with prostate cancer through bpMRI/TRUS fusion-guided biopsies of lesions with a Prostate Imaging Reporting and Data System (PI-RADS) category ≥ 3. These patients underwent robot-assisted laparoscopic radical prostatectomy (RARP) at our institution between July 2017 and March 2023. We examined the localization of preoperative and postoperative ITs, the highest Gleason score (GS), and tumor diameter in these patients. RESULTS: The preoperative cT stage matched the postoperative pT stage in 53 cases (47%), while 31 cases (27%) were upstaged, and 29 cases (26%) were downstaged (Weighted Kappa = 0.21). The preoperative and postoperative IT localizations were consistent in 97 cases (86%). The concordance rate between Gleason groups in targeted biopsies and RP specimens was 51%, with an upgrade in 25 cases (23%) and a downgrade in 27 cases (25%) (Weighted Kappa = 0.42). The maximum diameter of the IT and the maximum cancer core length on biopsy were correlated with the RP tumor's maximum diameter (p < 0.001 for both). CONCLUSION: The diagnostic accuracy of bpMRI/TRUS fusion biopsy is comparable to mpMRI, suggesting that it can be a cost-effective and time-saving alternative.

External validation of a four-tiered grading system for chromophobe renal cell carcinoma.

This study aimed to validate the prognostic value of a four-tiered grading system recently proposed by Avulova et al. and to explore the prognostic ability of another four-tiered classification grading system in which there is a separate Grade 3 for tumor necrosis. Grading of chromophobe renal cell carcinoma (ChRCC) by the Fuhrman system is not feasible because of the inherent nuclear atypia in ChRCC. We collected relevant data of 263 patients with ChRCC who had undergone surgery in our hospital from 2008 to 2020. The Kaplan-Meier method was used to calculate the survival rate and Cox proportional hazard regression models to assess associations with cancer-specific survival and distant metastasis-free survival by hazard ratios (HRs) and 95% confidence intervals (CIs). Ten patients died from ChRCC, and 12 developed metastases. The 5 year CSS rates were 95.9%. Grades 2 (HR = 10.9; CI 1.11-106.4; P = 0.04), 3 (HR = 33.6, CI 3.32-339.1; P = 0.003), and 4 (HR = 417.4, CI 35.0-4976.2; P < 0.001) in a four-tiered grading system were significantly associated with CSS in a multivariate setting. However, the difference in CSS between Grades 2 and 3 was not significant (HR = 2.14, 95% CI 0.43-10.63; P = 0.35). The HRs of the associations between an exploratory grading system that includes a separate Grade 3 for tumor necrosis and CSS were as follows: Grade 2, 10.2 (CI 1.06-97.9, P = 0.045); Grade 3, 11.4 (CI 1.18-109.6, P = 0.04); and Grade 4, 267.9 (CI 27.6-2603.3, P < 0.001). Similarly, Grades 2 and 3 did not differ significantly. The four-tiered grading system studied is useful for predicting death from ChRCC and metastasis. However, Grade 3 did not more accurately predict risk of death and metastasis than did Grade 2. This was also true for the novel exploratory grading system that classifies tumors with necrosis into a separate Grade 3.

The association between calreticulin and glucagon-like peptide-1 expressions with prognostic factors in high-grade gliomas.

OBJECTIVE: The aim of this study is to present the expressions of Calreticulin (CALR) and Glucagon-like peptide-1 (GLP-1) in high-grade gliomas and to further show the relation between the levels of these molecules and Ki-67 index, presence of Isocitrate dehydrogenase (IDH)-1 mutation, and tumor grade. PATIENTS AND METHODS: A total of 43 patients who underwent surgical resection due to high-grade gliomas (HGG) (grades III and IV) were included. The control group comprised 27 people who showed no gross pathology in the brain during the autopsy procedures. Adequately sized tumor samples were removed from each patient during surgery, and cerebral tissues were removed from the control subjects during the autopsy procedures. Each sample was stored at -80°C as rapidly as possible until the enzyme assay. RESULTS: Patients with high-grade gliomas showed significantly higher levels of CALR and significantly lower levels of GLP-1 when compared to control subjects (P = 0.001). CALR levels were significantly higher, GLP-1 levels were significantly lower in grade IV gliomas than those in grade III gliomas (P = 0.001). Gliomas with negative IDH-1 mutations had significantly higher CALR expressions and gliomas with positive IDH-1 mutations showed significantly higher GLP-1 expressions (P = 0.01). A positive correlation between Ki-67 and CALR and a negative correlation between Ki-67 and GLP-1 expressions were observed in grade IV gliomas (P = 0.001). CONCLUSIONS: Our results showed that higher CALR and lower GLP-1 expressions are found in HGGs compared to normal cerebral tissues.

Histologic grading of salivary gland mucoepidermoid carcinoma: A comparison of four grading systems and correlation with survival.

BACKGROUND: Mucoepidermoid carcinoma (MEC) is the most common malignant lesion of salivary glands. A number of histologic grading systems are in use for MEC with variable agreement between them. METHODS: This study was aimed at comparison of four grading systems for MEC: two qualitative (modified Healy and MSKCC grading) and two quantitative (AFIP and Brandwein grading). A retrospective search for diagnosed cases of MEC over eight years yielded 11 cases with adequate clinical details and histologic slides available for review. All cases were reviewed and graded as per the four grading systems. An inter-system agreement was assessed, and Kaplan-Meier analysis was performed to correlate the grading with clinical outcomes. RESULTS: A general agreement between all four grading systems was seen in 72.7% of cases. Brandwein grading assigned the highest percentage of high grades (18.2%), whereas Memorial Sloan-Kettering Cancer Center (MSKCC) assigned the highest percentage of low-grade MEC (72.7%). The agreement between MSKCC and modified Healy was highest at 90% of cases. There was generally a poor agreement between MSKCC and Brandwein grading systems. The MSKCC grading system showed a significant correlation with disease-free survival in MEC patients. CONCLUSION: Hence, the MSKCC grading system might serve as a better histologic grading system with a predictive value for the biologic behavior of the tumor. Further larger studies are required to validate these findings and implement the uniform use of MSKCC grading for MEC of salivary glands.

Accuracy of endometrial sampling in the diagnosis of endometrial cancer: a multicenter retrospective analysis of the JAGO-NOGGO.

BACKGROUND: Accurate preoperative molecular and histological risk stratification is essential for effective treatment planning in endometrial cancer. However, inconsistencies between pre- and postoperative tumor histology have been reported in previous studies. To address this issue and identify risk factors related to inaccurate histologic diagnosis after preoperative endometrial evaluation, we conducted this retrospective analysis. METHODS: We conducted a retrospective analysis involving 375 patients treated for primary endometrial cancer in five different gynaecological departments in Germany. Histological assessments of curettage and hysterectomy specimens were collected and evaluated. RESULTS: Preoperative histologic subtype was confirmed in 89.5% of cases and preoperative tumor grading in 75.2% of cases. Higher rates of histologic subtype variations (36.84%) were observed for non-endometrioid carcinomas. Non-endometrioid (OR 4.41) histology and high-grade (OR 8.37) carcinomas were identified as predictors of diverging histologic subtypes, while intermediate (OR 5.04) and high grading (OR 3.94) predicted diverging tumor grading. CONCLUSION: When planning therapy for endometrial cancer, the limited accuracy of endometrial sampling, especially in case of non-endometrioid histology or high tumor grading, should be carefully considered.

Contemporary Diagnostic Reporting for Prostatic Adenocarcinoma: Morphologic Aspects, Molecular Correlates, and Management Perspectives.

The diagnosis and reporting of prostatic adenocarcinoma have evolved from the classic framework promulgated by Dr Donald Gleason in the 1960s into a complex and nuanced system of grading and reporting that nonetheless retains the essence of his remarkable observations. The criteria for the "Gleason patterns" originally proposed have been continually refined by consensuses in the field, and Gleason scores have been stratified into a patient-friendly set of prognostically validated and widely adopted Grade Groups. One product of this successful grading approach has been the opportunity for pathologists to report diagnoses that signal carefully personalized management, placing the surgical pathologist's interpretation at the center of patient care. At one end of the continuum of disease aggressiveness, personalized diagnostic care means to sub-stratify patients with more indolent disease for active surveillance, while at the other end of the continuum, reporting histologic markers signaling aggression allows sub-stratification of clinically significant disease. Whether contemporary reporting parameters represent deeper nuances of more established ones (eg, new criteria and/or quantitation of Gleason patterns 4 and 5) or represent additional features reported alongside grade (intraductal carcinoma, cribriform patterns of carcinoma), assessment and grading have become more complex and demanding. Herein, we explore these newer reporting parameters, highlighting the state of knowledge regarding morphologic, molecular, and management aspects. Emphasis is made on the increasing value and stakes of histopathologists' interpretations and reporting into current clinical risk stratification and treatment guidelines.

Significance of the cribriform morphology area ratio for biochemical recurrence in Gleason score 4 + 4 prostate cancer patients following robot-assisted radical prostatectomy.

BACKGROUND: In prostate cancer, histological cribriform patterns are categorized as Gleason pattern 4, and recent studies have indicated that their size and percentage are associated with the risk of biochemical recurrence (BCR). However, these studies included a mixture of cases with various Gleason scores (GSs). We therefore examined the prognostic value of the area and percentage of cribriform patterns in patients with GS 4 + 4 prostate cancer. METHODS: We investigated 108 patients with GS 4 + 4 prostate cancer who underwent robot-assisted radical prostatectomy (RARP). After digitally scanning the hematoxylin and eosin-stained slides, we measured the area of the entire cancer and cribriform patterns. Predictive factors for BCR were explored using log-rank test and Cox proportional hazard model analyses. RESULTS: Sixty-seven (62.0%) patients had a cribriform pattern in RARP specimens, and 32 (29.6%) experienced BCR. The median total cancer area, cribriform pattern area, and percentage of cribriform pattern area (% cribriform) were 427.70 mm2 (interquartile range [IQR], 171.65-688.53 mm2 ), 8.85 mm2 (IQR, 0-98.83 mm2 ), and 2.44% (IQR, 0%-33.70%), respectively. Univariate analyses showed that higher preoperative serum prostate-specific antigen (PSA) levels, positive resection margins, advanced pathological T stage, extraprostatic extension, larger total cancer area, larger cribriform morphology area, and higher % cribriform values were significantly associated with BCR. A multivariate analysis demonstrated that the PSA level (hazard ratio [HR], 1.061; 95% confidence interval [CI], 1.011-1.113; p = 0.017) and % cribriform (HR, 1.018; 95% CI, 1.005-1.031; p = 0.005) were independent predictors of BCR. CONCLUSIONS: An increased % cribriform value was associated with BCR in patients with GS 4 + 4 prostate cancer following RARP.

Incorporating PHI in decision making: external validation of the Rotterdam risk calculators for detection of prostate cancer.

PURPOSE: External validation of existing risk calculators (RC) to assess the individualized risk of detecting prostate cancer (PCa) in prostate biopsies is needed to determine their clinical usefulness. The objective was to externally validate the Rotterdam Prostate Cancer RCs 3 and 4 (RPCRC-3/4) and that incorporating PHI (RPCRC-PHI) in a contemporary Spanish cohort. METHODS: Multicenter prospective study that included patients suspicious of harboring PCa. Men who attended the urology consultation were tested for PHI before prostate biopsy. To evaluate the performance of the prediction models: discrimination (receiver operating characteristic (ROC) curves), calibration and net benefit [decision curve analysis (DCA)] were calculated. These analyses were carried out for detection of any PCa and clinically significant (cs)PCa, defined as ISUP grade ≥ 2. RESULTS: Among the 559 men included, 337 (60.28%) and 194 (34.7%) were diagnosed of PCa and csPCa, respectively. RPCRC-PHI had the best discrimination ability for detection of PCa and csPCa with AUCs of 0.85 (95%CI 0.82-0.88) and 0.82 (95%CI 0.78-0.85), respectively. Calibration plots showed that RPCRC-3/4 underestimates the risk of detecting PCa showing the need for recalibration. In DCA, RPCRC-PHI shows the highest net benefit compared to biopsy all men. CONCLUSIONS: The RPCRC-PHI performed properly in a contemporary clinical setting, especially for prediction of csPCa.

Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology.

This study aimed to enhance the accuracy of Gleason grade group (GG) upgrade prediction in prostate cancer (PCa) patients who underwent MRI-guided in-bore biopsy (MRGB) and radical prostatectomy (RP) through a combined analysis of prebiopsy and MRGB clinical data. A retrospective analysis of 95 patients with prostate cancer diagnosed by MRGB was conducted where all patients had undergone RP. Among the patients, 64.2% had consistent GG results between in-bore biopsies and RP, whereas 28.4% had upgraded and 7.4% had downgraded results. GG1 biopsy results, lower biopsy core count, and fewer positive cores were correlated with upgrades in the entire patient group. In patients with GG > 1 , larger tumor sizes and fewer biopsy cores were associated with upgrades. By integrating MRGB data with prebiopsy clinical data, machine learning (ML) models achieved 85.6% accuracy in predicting upgrades, surpassing the 64.2% baseline from MRGB alone. ML analysis also highlighted the value of the minimum apparent diffusion coefficient ( ADC min ) for GG > 1 patients. Incorporation of MRGB results with tumor size, ADC min value, number of biopsy cores, positive core count, and Gleason grade can be useful to predict GG upgrade at final pathology and guide patient selection for active surveillance.

Retrospective Analyses of PD-L1, LAG-3, TIM-3, OX40L Expressions and MSI Status in Gastroenteropancreatic Neuroendocrine Neoplasms.

We investigated expressions of PD-L1, LAG-3, TIM-3, and OX40L as immune checkpoint proteins, and MSI (repetitive short-DNA-sequences due to defective DNA-repair system) status were analyzed with immunohistochemistry from tissue blocks. Of 83 patients, PD-L1 expression was observed in 18.1% (n = 15) of the patients. None of the patients exhibited LAG-3 expression. TIM-3 expression was 4.9% (n = 4), OX40L was 22.9% (n = 19), and 8.4% (n = 7) of the patients had MSI tumor. A low-to-intermediate positive correlation was observed between PD-L1 and TIM-3 expressions (rho: 0.333, p < 0.01). Although PD-L1 expression was higher in grade 3 NET/NEC, MSI status was prominent in grade 1/2 NET.

Prediction of Gleason score in prostate cancer patients based on radiomic features of transrectal ultrasound images.

OBJECTIVES: The aim of this study was to develop a model for predicting the Gleason score of patients with prostate cancer based on ultrasound images. METHODS: Transrectal ultrasound images of 838 prostate cancer patients from The Cancer Imaging Archive database were included in this cross-section study. Data were randomly divided into the training set and testing set (ratio 7:3). A total of 103 radiomic features were extracted from the ultrasound image. Lasso regression was used to select radiomic features. Random forest and broad learning system (BLS) methods were utilized to develop the model. The area under the curve (AUC) was calculated to evaluate the model performance. RESULTS: After the screening, 10 radiomic features were selected. The AUC and accuracy of the radiomic feature variables random forest model in the testing set were 0.727 (95% CI, 0.694-0.760) and 0.646 (95% CI, 0.620-0.673), respectively. When PSA and radiomic feature variables were included in the random forest model, the AUC and accuracy of the model were 0.770 (95% CI, 0.740-0.800) and 0.713 (95% CI, 0.688-0.738), respectively. While the BLS method was utilized to construct the model, the AUC and accuracy of the model were 0.726 (95% CI, 0.693-0.759) and 0.698 (95% CI, 0.673-0.723), respectively. In predictions for different Gleason grades, the highest AUC of 0.847 (95% CI, 0.749-0.945) was found to predict Gleason grade 5 (Gleason score ≥9). CONCLUSIONS: A model based on transrectal ultrasound image features showed a good ability to predict Gleason scores in prostate cancer patients. ADVANCES IN KNOWLEDGE: This study used ultrasound-based radiomics to predict the Gleason score of patients with prostate cancer.

International Society of Urological Pathology Consensus Conference on Current Issues in Bladder Cancer: Main Conclusions and Recommendations.

The 2022 International Society of Urological Pathology consensus conference on current issues in bladder cancer made recommendations regarding adoption of a three-tier grading system, grading of cancers with grade heterogeneity, grading and reporting of bladder cancers with subtype/divergent differentiation, and mandatory subcategorisation of T1 bladder cancers.

CircUBE3A(2,3,4,5) promotes adenylate-uridylate-rich binding factor 1 nuclear translocation to suppress prostate cancer metastasis.

Metastatic progression is the primary cause of mortality in prostate cancer (PCa) patients. Although circular RNAs (circRNAs) have been implicated in cancer progression and metastasis, our current understanding of their role in PCa metastasis remains limited. In this study, we identified that circUBE3A(2,3,4,5), which originated from exons 2, 3, 4 and 5 of the human ubiquitin-protein ligase E3A (UBE3A) gene, was specifically downregulated in PCa tissues and correlated with the Gleason score, bone metastasis, and D'Amico risk classification. Through the in vitro and in vivo experiments, we demonstrated that overexpression of circUBE3A(2,3,4,5) inhibited PCa cell migration, invasion, metastasis, and proliferation. Mechanistically, circUBE3A(2,3,4,5) was found to bind to adenylate-uridylate-rich binding factor 1 (AUF1), promoting the translocation of AUF1 into the nucleus. This led to decreased AUF1 in the cytoplasm, resulting in methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) mRNA instability and a subsequent reduction at the protein level. The downregulation of MTHFD2 further inhibited vimentin expression, thereby suppressing PCa cell epithelial-mesenchymal transition. Additionally, two pairs of the short-inverted repeats (TSIRs) in flanking introns were identified to synergistically facilitate the generation of circUBE3A(2,3,4,5) and other circRNAs. In summary, TSIRs-induced circUBE3A(2,3,4,5) acts as a suppressor of PCa metastasis by enhancing AUF1 nuclear translocation, reducing MTHFD2, and subsequently inhibiting vimentin expression. This study characterizes circUBE3A(2,3,4,5) as a functional circRNA and proposes it as a highly promising target for preventing PCa metastasis.

Positive correlation between the nuclear expression of GPER and pGLI3 in prostate cancer tissues from patients with different Gleason scores.

Prostate cancer (PCa) is the most prevalent cause of death in the male population worldwide. The G Protein-Coupled Estrogen Receptor (GPER) has been gaining relevance in the development of PCa. Hedgehog (Hh) pathway activation is associated with aggressiveness, metastasis, and relapse in PCa patients. To date, no studies have evaluated the crosstalk between the GPER and the Hh pathway along different group grades in PCa. We conducted an analysis of paraffin-embedded tissues derived from patients with different prognostic grade of PCa using immunohistochemistry. Expression and correlation between GPER and glioma associated oncogene homologue (GLI) transcriptional factors in the parenchyma and stroma of PCa tumors were evaluated. Our results indicate that GPER is highly expressed in the nucleus and increases with higher grade groups. Additionally, GPER's expression correlates with pGLI3 nuclear expression across different grade groups in PCa tissues; however, whether the receptor induces the activation of GLI transcriptional factors, or the latter modulate the expression of GPER is yet to be discovered, as well as the functional consequence of this correlation.

Differential diagnostic value of 3.0T MR 3D-ASL technique for recurrence and pseudo-progression of high-grade glioma.

OBJECTIVE: This study aimed to explore the value of 3.0T magnetic resonance three-dimensional arterial spin labeling imaging (3D-ASL) technology in the differential diagnosis of recurrence and pseudo-progression of high-grade gliomas. PATIENTS AND METHODS: Fifty patients with high-grade glioma were selected as research objects. All 50 patients were examined by magnetic resonance imaging (MRI), and the lesions were found to be enlarged or abnormally enhanced. All the patients were examined using the 3.0T MR 3D-ASL technique. With targeted biopsy pathology as the gold standard, the diagnostic results of the 3.0T MR 3D-ASL technique were analyzed, and the cerebral blood flow (rCBFmax) ratio was compared between patients with recurrent glioma and patients with pseudo-progression [maximum blood flow value/contralateral mirror area (CBFmax/contralateral mirror area), CBFmax/contralateral white matter, CBFmax/contralateral gray matter]. RESULTS: Among 50 glioma patients, 31 (62.00%) were diagnosed with recurrence through pathological examination, and 19 (38.00%) were diagnosed with pseudo-progression. 30 patients with recurrence (60.00%) and 20 patients with pseudo-progression (40.00%) were diagnosed using 3.0T magnetic resonance 3D-ASL technology. The diagnostic accuracy of 3.0T magnetic resonance 3D-ASL technology was 96.77% (30/31) (p > 0.05). Using pathological results as the "gold standard", the relevant parameters of 3.0T magnetic resonance 3D-ASL technology under different pathological results were analyzed. The results showed that the CBFmax/contralateral mirror area, CBFmax/contralateral white matter, and CBFmax/contralateral gray matter ratios of advanced glioma recurrence patients were significantly higher than those of pseudo-progression (p < 0.05). CONCLUSIONS: The application of 3.0T MR 3D-ASL in high-grade glioma can effectively distinguish recurrence and pseudo-progression, with significant diagnostic value.

Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience.

BACKGROUND: The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI). PATIENTS AND METHODS: We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded. RESULTS: There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively). CONCLUSIONS: Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks.

Gliomas are primary brain tumors caused by glial cells. These cancers' classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.