Surgical Outcome of Open Radical Prostatectomy in Nigeria: A Five-Years Single-Surgeon Experience.

BACKGROUND: Organ-confined prostate cancer is curable through surgical treatment by radical prostatectomy. AIM: To report initial outcomes of open radical prostatectomy in Nigeria from 2014 to 2019. METHODS: Open radical prostatectomy in private hospital settings. Thirty-five patients underwent open radical prostatectomy in private hospital settings from 2014 to 2019. A retrospective study of the case notes was undertaken. RESULTS: The age range was 56-77 years (mean: 67.7 ± 5.6 years); presenting total PSA 7.3-32.0 ng/ml (mean: 16.2 ± 6.4); Gleason score range 6-10 and clinical stage T2c. Mean operation duration 192.4 ± 52.0 min. All patients received blood transfusion (average blood transfusion 4.58 ± 1.9 pints). The median length of hospital stay was 7 days and the catheterization duration was 16.6 days. The Gleason score ranges from 6 to 10. Biopsy and specimen histology Gleason scores correlated in all cases. Biochemical relapse within 1 year occurred in 12 (34.3%) patients. Adequate PSA control was achieved in 23 (65.7%) patients. Two cancer-related deaths occurred within 2 years of surgery. All patients voided well following removal of the catheter; persisting mild stress urinary incontinence resolved on conservative measures within 3-6 months. Anastomotic stricture occurred in one patient 1 (2.9%) in this present. Information on preoperative potency rate was unavailable; however, postoperation, 11 (31.4%) patients achieved erections sufficient for intercourse with oral therapy. All surviving 33 (94.3%) patients reported satisfactory performance status. CONCLUSIONS: Open radical prostatectomy was successfully performed in all the patients. Reasonable, comparative functional, and oncological outcomes were achieved during the study period.

Identifying, Diagnosing, and Grading Malignant Peripheral Nerve Sheath Tumors in Genetically Engineered Mouse Models.

Patients with the autosomal dominant tumor susceptibility syndrome neurofibromatosis type 1 (NF1) commonly develop plexiform neurofibromas (PNs) that subsequently transform into highly aggressive malignant peripheral nerve sheath tumors (MPNSTs). Understanding the process by which a PN transforms into an MPNST would be facilitated by the availability of genetically engineered mouse (GEM) models that accurately replicate the PN-MPNST progression seen in humans with NF1. Unfortunately, GEM models with Nf1 ablation do not fully recapitulate this process. This led us to develop P0-GGFß3 mice, a GEM model in which overexpression of the Schwann cell mitogen neuregulin-1 (NRG1) in Schwann cells results in the development of PNs that progress to become MPNSTs with high frequency. However, to determine whether tumorigenesis and neoplastic progression in P0-GGFß3 mice accurately model the processes seen in NF1 patients, we had to first prove that the pathology of P0-GGFß3 peripheral nerve sheath tumors recapitulates the pathology of their human counterparts. Here, we describe the specialized methodologies used to accurately diagnose and grade peripheral nervous system neoplasms in GEM models, using P0-GGFß3 and P0-GGFß3;Trp53+/- mice as an example. We describe the histologic, immunohistochemical, and histochemical methods used to diagnose PNs and MPNSTs, how to distinguish these neoplasms from other tumor types that mimic their pathology, and how to grade these neoplasms. We discuss the establishment of early-passage cultures from GEM MPNSTs, how to characterize these cultures using immunocytochemistry, and how to verify their tumorigenicity by establishing allografts. Collectively, these techniques characterize the pathology of PNs and MPNSTs that arise in GEM models and critically compare the pathology of these murine tumors to their human counterparts.

Mitochondrial RNA modification-based signature to predict prognosis of lower grade glioma: a multi-omics exploration and verification study.

Mitochondrial RNA modification (MRM) plays a crucial role in regulating the expression of key mitochondrial genes and promoting tumor metastasis. Despite its significance, comprehensive studies on MRM in lower grade gliomas (LGGs) remain unknown. Single-cell RNA-seq data (GSE89567) was used to evaluate the distribution functional status, and correlation of MRM-related genes in different cell types of LGG microenvironment. We developed an MRM scoring system by selecting potential MRM-related genes using LASSO regression analysis and the Random Survival Forest algorithm, based on multiple bulk RNA-seq datasets from TCGA, CGGA, GSE16011, and E-MTAB-3892. Analysis was performed on prognostic and immunological features, signaling pathways, metabolism, somatic mutations and copy number variations (CNVs), treatment responses, and forecasting of potential small-molecule agents. A total of 35 MRM-related genes were selected from the literature. Differential expression analysis of 1120 normal brain tissues and 529 LGGs revealed that 22 and 10 genes were upregulated and downregulated, respectively. Most genes were associated with prognosis of LGG. METLL8, METLL2A, TRMT112, and METTL2B were extensively expressed in all cell types and different cell cycle of each cell type. Almost all cell types had clusters related to mitochondrial RNA processing, ribosome biogenesis, or oxidative phosphorylation. Cell-cell communication and Pearson correlation analyses indicated that MRM may promoting the development of microenvironment beneficial to malignant progression via modulating NCMA signaling pathway and ICP expression. A total of 11 and 9 MRM-related genes were observed by LASSO and the RSF algorithm, respectively, and finally 6 MRM-related genes were used to establish MRM scoring system (TRMT2B, TRMT11, METTL6, METTL8, TRMT6, and TRUB2). The six MRM-related genes were then validated by qPCR in glioma and normal tissues. MRM score can predict the malignant clinical characteristics, abundance of immune infiltration, gene variation, clinical outcome, the enrichment of signaling pathways and metabolism. In vitro experiments demonstrated that silencing METTL8 significantly curbs glioma cell proliferation and enhances apoptosis. Patients with a high MRM score showed a better response to immunotherapies and small-molecule agents such as arachidonyl trifluoromethyl ketone, MS.275, AH.6809, tacrolimus, and TTNPB. These novel insights into the biological impacts of MRM within the glioma microenvironment underscore its potential as a target for developing precise therapies, including immunotherapeutic approaches.

The interplay mechanism between IDH mutation, MGMT-promoter methylation, and PRMT5 activity in the progression of grade 4 astrocytoma: unraveling the complex triad theory.

Background: The dysregulation of Isocitrate dehydrogenase (IDH) and the subsequent production of 2-Hydroxyglutrate (2HG) may alter the expression of epigenetic proteins in Grade 4 astrocytoma. The interplay mechanism between IDH, O-6-methylguanine-DNA methyltransferase (MGMT)-promoter methylation, and protein methyltransferase proteins-5 (PRMT5) activity, with tumor progression has never been described. Methods: A retrospective cohort of 34 patients with G4 astrocytoma is classified into IDH-mutant and IDH-wildtype tumors. Both groups were tested for MGMT-promoter methylation and PRMT5 through methylation-specific and gene expression PCR analysis. Inter-cohort statistical significance was evaluated. Results: Both IDH-mutant WHO grade 4 astrocytomas (n = 22, 64.7%) and IDH-wildtype glioblastomas (n = 12, 35.3%) had upregulated PRMT5 gene expression except in one case. Out of the 22 IDH-mutant tumors, 10 (45.5%) tumors showed MGMT-promoter methylation and 12 (54.5%) tumors had unmethylated MGMT. All IDH-wildtype tumors had unmethylated MGMT. There was a statistically significant relationship between MGMT-promoter methylation and IDH in G4 astrocytoma (p-value = 0.006). Statistically significant differences in progression-free survival (PFS) were also observed among all G4 astrocytomas that expressed PRMT5 and received either temozolomide (TMZ) or TMZ plus other chemotherapies, regardless of their IDH or MGMT-methylation status (p-value=0.0014). Specifically, IDH-mutant tumors that had upregulated PRMT5 activity and MGMT-promoter methylation, who received only TMZ, have exhibited longer PFS. Conclusions: The relationship between PRMT5, MGMT-promoter, and IDH is not tri-directional. However, accumulation of D2-hydroxyglutarate (2-HG), which partially activates 2-OG-dependent deoxygenase, may not affect their activities. In IDH-wildtype glioblastomas, the 2HG-2OG pathway is typically inactive, leading to PRMT5 upregulation. TMZ alone, compared to TMZ-plus, can increase PFS in upregulated PRMT5 tumors. Thus, using a PRMT5 inhibitor in G4 astrocytomas may help in tumor regression.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.

BACKGROUND: Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens. METHODS: A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. RESULTS: Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79). CONCLUSIONS: DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Real-world treatment patterns and clinical outcomes in patients with prostate cancer.: A single institution experience in Saudi Arabia.

OBJECTIVES: To describe the current real-world treatment landscape, sequence of therapies, and outcomes in patients with prostate cancer (PC). METHODS: A retrospective cohort study for PC patients diagnosed at King Abdullah Medical City Cancer Center in Makkah, Saudi Arabia, between January 2011 and December 2021. Data extracted from electronic medical records. RESULTS: A total of 282 patients with PC, with a mean age of 70 years and body mass index of 27. Among them, 274 (99%) had no family history of cancer, while 164 (58%) had hypertension and 125 (44%) had diabetes mellitus. Adenocarcinoma was the most common histology, found in 275 (97%) patients, with 99 (35%) having a Gleason score of 9. Notably, 184 (65%) patients presented with metastatic disease, and 147 (52%) with bone metastasis. While 198 (70%) patients underwent surgery, 184 (65%) did not receive radiotherapy. The most common first-line metastatic therapy was abiraterone in 23 (8%) patients, followed by enzalutamide in 7 (2.5%). During the study period, 167 (59%) patients survived, with an average treatment duration of 2.5 years. CONCLUSION: This study provides insights into real-world treatment patterns and clinical outcomes in patients with PC. The findings of this study highlight the importance of adhering to treatment standards and making informed clinical decisions.

Radiomic white matter parameters of functional integrity of the corticospinal tract in high-grade glioma.

Tractography has become a widely available tool for the planning of neurosurgical operations as well as for neuroscientific research. The absence of patient interaction makes it easily applicable. However, it leaves uncertainty about the functional relevance of the identified bundles. We retrospectively analyzed the correlation of white matter markers with their clinical function in 24 right-handed patients who underwent first surgery for high-grade glioma. Morphological affection of the corticospinal tract (CST) and grade of paresis were assessed before surgery. Tractography was performed manually with MRTrix3 and automatically with TractSeg. Median and mean fractional anisotropy (FA) from manual tractography showed a significant correlation with CST affection (p = 0.008) and paresis (p = 0.015, p = 0.026). CST affection correlated further most with energy, and surface-volume ratio (p = 0.014) from radiomic analysis. Paresis correlated most with maximum 2D column diameter (p = 0.005), minor axis length (p = 0.006), and kurtosis (p = 0.008) from radiomic analysis. Streamline count yielded no significant correlations. In conclusion, mean or median FA can be used for the assessment of CST integrity in high-grade glioma. Also, several radiomic parameters are suited to describe tract integrity and may be used to quantitatively analyze white matter in the future.

Capmatinib is an effective treatment for MET-fusion driven pediatric high-grade glioma and synergizes with radiotherapy.

BACKGROUND: Pediatric-type diffuse high-grade glioma (pHGG) is the most frequent malignant brain tumor in children and can be subclassified into multiple entities. Fusion genes activating the MET receptor tyrosine kinase often occur in infant-type hemispheric glioma (IHG) but also in other pHGG and are associated with devastating morbidity and mortality. METHODS: To identify new treatment options, we established and characterized two novel orthotopic mouse models harboring distinct MET fusions. These included an immunocompetent, murine allograft model and patient-derived orthotopic xenografts (PDOX) from a MET-fusion IHG patient who failed conventional therapy and targeted therapy with cabozantinib. With these models, we analyzed the efficacy and pharmacokinetic properties of three MET inhibitors, capmatinib, crizotinib and cabozantinib, alone or combined with radiotherapy. RESULTS: Capmatinib showed superior brain pharmacokinetic properties and greater in vitro and in vivo efficacy than cabozantinib or crizotinib in both models. The PDOX models recapitulated the poor efficacy of cabozantinib experienced by the patient. In contrast, capmatinib extended survival and induced long-term progression-free survival when combined with radiotherapy in two complementary mouse models. Capmatinib treatment increased radiation-induced DNA double-strand breaks and delayed their repair. CONCLUSIONS: We comprehensively investigated the combination of MET inhibition and radiotherapy as a novel treatment option for MET-driven pHGG. Our seminal preclinical data package includes pharmacokinetic characterization, recapitulation of clinical outcomes, coinciding results from multiple complementing in vivo studies, and insights into molecular mechanism underlying increased efficacy. Taken together, we demonstrate the groundbreaking efficacy of capmatinib and radiation as a highly promising concept for future clinical trials.

Survival outcome and predictors of WHO grade 2 and 3 insular gliomas: A classification based on the tumor spread.

OBJECTIVE: The study aimed to identify if clinical features and survival outcomes of insular glioma patients are associated with our classification based on the tumor spread. METHODS: Our study included 283 consecutive patients diagnosed with histological grade 2 and 3 insular gliomas. A new classification was proposed, and tumors restricted to the paralimbic system were defined as type 1. When tumors invaded the limbic system (referred to as the hippocampus and its surrounding structures in this study) simultaneously, they were defined as type 2. Tumors with additional internal capsule involvement were defined as type 3. RESULTS: Tumors defined as type 3 had a higher age at diagnosis (p = 0.002) and a higher preoperative volume (p < 0.001). Furthermore, type 3 was more likely to be diagnosed as IDH wild type (p < 0.001), with a higher rate of Ki-67 index (p = 0.015) and a lower rate of gross total resection (p < 0.001). Type 1 had a slower tumor growth rate than type 2 (mean 3.3%/month vs. 19.8%/month; p < 0.001). Multivariate Cox regression analysis revealed the extent of resection (HR 0.259, p = 0.004), IDH status (HR 3.694, p = 0.012), and tumor spread type (HR = 1.874, p = 0.012) as independent predictors of overall survival (OS). Tumor grade (HR 2.609, p = 0.008), the extent of resection (HR 0.488, p = 0.038), IDH status (HR 2.225, p = 0.025), and tumor spread type (HR 1.531, p = 0.038) were significant in predicting progression-free survival (PFS). CONCLUSION: The current study proposes a classification of the insular glioma according to the tumor spread. It indicates that the tumors defined as type 1 have a relatively better nature and biological characteristics, and those defined as type 3 can be more aggressive and refractory. Besides its predictive value for prognosis, the classification has potential value in formulating surgical strategies for patients with insular gliomas.

The Glasgow Microenvironment Score: an exemplar of contemporary biomarker evolution in colorectal cancer.

Colorectal cancer remains a leading cause of mortality worldwide. Significant variation in response to treatment and survival is evident among patients with similar stage disease. Molecular profiling has highlighted the heterogeneity of colorectal cancer but has had limited impact in daily clinical practice. Biomarkers with robust prognostic and therapeutic relevance are urgently required. Ideally, biomarkers would be derived from H&E sections used for routine pathological staging, have reliable sensitivity and specificity, and require minimal additional training. The biomarker targets would capture key pathological features with proven additive prognostic and clinical utility, such as the local inflammatory response and tumour microenvironment. The Glasgow Microenvironment Score (GMS), first described in 2014, combines assessment of peritumoural inflammation at the invasive margin with quantification of tumour stromal content. Using H&E sections, the Klintrup-Mäkinen (KM) grade is determined by qualitative morphological assessment of the peritumoural lymphocytic infiltrate at the invasive margin and tumour stroma percentage (TSP) calculated in a semi-quantitative manner as a percentage of stroma within the visible field. The resulting three prognostic categories have direct clinical relevance: GMS 0 denotes a tumour with a dense inflammatory infiltrate/high KM grade at the invasive margin and improved survival; GMS 1 represents weak inflammatory response and low TSP associated with intermediate survival; and GMS 2 tumours are typified by a weak inflammatory response, high TSP, and inferior survival. The prognostic capacity of the GMS has been widely validated while its potential to guide chemotherapy has been demonstrated in a large phase 3 trial cohort. Here, we detail its journey from conception through validation to clinical translation and outline the future for this promising and practical biomarker.

Automatic analysis of nuclear features reveals a non-tumoral predictor of tumor grade in bladder cancer.

BACKGROUND & OBJECTIVES: Tumor grade determines prognosis in urothelial carcinoma. The classification of low and high grade is based on nuclear morphological features that include nuclear size, hyperchromasia and pleomorphism. These features are subjectively assessed by the pathologists and are not numerically measured, which leads to high rates of interobserver variability. The purpose of this study is to assess the value of a computer-based image analysis tool for identifying predictors of tumor grade in bladder cancer. METHODS: Four hundred images of urothelial tumors were graded by five pathologists and two expert genitourinary pathologists using a scale of 1 (lowest grade) to 5 (highest grade). A computer algorithm was used to automatically segment the nuclei and to provide morphometric parameters for each nucleus, which were used to establish the grading algorithm. Grading algorithm was compared to pathologists' agreement. RESULTS: Comparison of the grading scores of the five pathologists with the expert genitourinary pathologists score showed agreement rates between 88.5% and 97.5%.The agreement rate between the two expert genitourinary pathologists was 99.5%. The quantified algorithm based conventional parameters that determine the grade (nuclear size, pleomorphism and hyperchromasia) showed > 85% agreement with the expert genitourinary pathologists. Surprisingly, the parameter that was most associated with tumor grade was the 10th percentile of the nuclear area, and high grade was associated with lower 10th percentile nuclei, caused by the presence of more inflammatory cells in the high-grade tumors. CONCLUSION: Quantitative nuclear features could be applied to determine urothelial carcinoma grade and explore new biologically explainable parameters with better correlation to grade than those currently used.

Ultrasound contrast-enhanced radiomics model for preoperative prediction of the tumor grade of clear cell renal cell carcinoma: an exploratory study.

BACKGROUND: This study aims to explore machine learning(ML) methods for non-invasive assessment of WHO/ISUP nuclear grading in clear cell renal cell carcinoma(ccRCC) using contrast-enhanced ultrasound(CEUS) radiomics. METHODS: This retrospective study included 122 patients diagnosed as ccRCC after surgical resection. They were divided into a training set (n = 86) and a testing set(n = 36). CEUS radiographic features were extracted from CEUS images, and XGBoost ML models (US, CP, and MP model) with independent features at different phases were established. Multivariate regression analysis was performed on the characteristics of different radiomics phases to determine the indicators used for developing the prediction model of the combined CEUS model and establishing the XGBoost model. The training set was used to train the above four kinds of radiomics models, which were then tested in the testing set. Radiologists evaluated tumor characteristics, established a CEUS reading model, and compared the diagnostic efficacy of CEUS reading model with independent characteristics and combined CEUS model prediction models. RESULTS: The combined CEUS radiomics model demonstrated the best performance in the training set, with an area under the curve (AUC) of 0.84, accuracy of 0.779, sensitivity of 0.717, specificity of 0.879, positive predictive value (PPV) of 0.905, and negative predictive value (NPV) of0.659. In the testing set, the AUC was 0.811, with an accuracy of 0.784, sensitivity of 0.783, specificity of 0.786, PPV of 0.857, and NPV of 0.688. CONCLUSIONS: The radiomics model based on CEUS exhibits high accuracy in non-invasive prediction of ccRCC. This model can be utilized for non-invasive detection of WHO/ISUP nuclear grading of ccRCC and can serve as an effective tool to assist clinical decision-making processes.

Can a nomogram predict apical prostate cancer pathology upgrade from fusion biopsy to final pathology? A multicenter study.

BACKGROUND: This study evaluates the efficacy of a nomogram for predicting the pathology upgrade of apical prostate cancer (PCa). METHODS: A total of 754 eligible patients were diagnosed with apical PCa through combined systematic and magnetic resonance imaging (MRI)-targeted prostate biopsy followed by radical prostatectomy (RP) were retrospectively identified from two hospitals (training: 754, internal validation: 182, internal-external validation: 148). A nomogram for the identification of apical tumors in high-risk pathology upgrades through comparing the results of biopsy and RP was established incorporating statistically significant risk factors based on univariable and multivariable logistic regression. The nomogram's performance was assessed via the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). RESULTS: Univariable and multivariable analysis identified age, targeted biopsy, number of targeted cores, TNM stage, and the prostate imaging-reporting and data system score as significant predictors of apical tumor pathological progression. Our nomogram, based on these variables, demonstrated ROC curves for pathology upgrade with values of 0.883 (95% CI, 0.847-0.929), 0.865 (95% CI, 0.790-0.945), and 0.840 (95% CI, 0.742-0.904) for the training, internal validation and internal-external validation cohorts respectively. Calibration curves showed good consistency between the predicted and actual outcomes. The validation groups also showed great generalizability with the calibration curves. DCA results also demonstrated excellent performance for our nomogram with positive benefit across a threshold probability range of 0-0.9 for the training and internal validation group, and 0-0.6 for the internal-external validation group. CONCLUSION: The nomogram, integrating clinical, radiological, and pathological data, effectively predicts the risk of pathology upgrade in apical PCa tumors. It holds significant potential to guide clinicians in optimizing the surgical management of these patients.

Active surveillance of low-grade prostate cancer using the SurACaP Criteria: A multi-institutional series with a median follow-up of 10years.

PURPOSE: To report on the oncological outcomes of active surveillance (AS) in low-grade prostate cancer (PCa) patients using the French SurACaP protocol, with a focus on long-term outcomes. METHODS: This multicenter study recruited patients with low-grade PCa between 2007 and 2013 in four referral centers in France. The cohort included patients meeting the SurACaP inclusion criteria, i.e., aged ≤75years, with low-grade PCa (i.e., ISUP 1), clinical stage T1c/T2a, PSA ≤10ng/mL and ≤3 positive cores and tumor length ≤3mm per core. The SurACaP protocol included a digital rectal examination every six months, PSA level measurement every three months for the first two years after inclusion and twice a year thereafter, a confirmatory biopsy in the first year after inclusion, and then follow-up biopsy every two years or if disease progression was suspected. Multiparametric magnetic resonance imaging (mpMRI) was progressively included over the study period. RESULTS: A total of 86 consecutive patients were included, with a median follow-up of 10.6 years. Only one patient developed metastases and died of PCa. The estimated rates of grade reclassification and treatment-free survival at 15 years were 53.4% and 21.2%, respectively. A negative mpMRI at baseline and a negative confirmatory biopsy were significantly associated with a lower risk of disease progression (P<0.05). CONCLUSIONS: AS using the French SurACaP protocol is a safe and valuable strategy for patients with low-risk PCa, with excellent oncological outcomes after more than 10 years' follow-up. Future studies are crucial to broaden the inclusion criteria and develop a personalized, risk based AS protocol with the aim of de-escalating follow-up examinations. LEVEL OF EVIDENCE: Grade 4.

Cell Cycle Checkpoints p16 and p21-Strong Predictors of Clinicopathologic Outcomes in High-Grade Osteosarcoma.

PURPOSE: In this study, we used a series of immunohistochemical measurements of 2 cell cycle regulators, p16 and p21, to evaluate their prognostic value, separately and in combination, for the disease outcomes. METHOD: A total of 101 patients with high-grade osteosarcoma were included in this study. Clinicopathologic data were collected, and immunohistochemistry for p16 and p21 was performed and interpreted by 3 independent pathologists. Statistical analysis was performed to assess the strength of each of these markers relative to disease outcome. RESULTS: Our results indicate that more than 90% expression (high) of p16 by immunohistochemistry on the initial biopsy has a strong predictive value for good histologic response to chemotherapy. The patients are also more likely to survive the past 5 years and less likely to develop metastasis than patients with less than 90% p16 (low) expression. The results for p21, on the other hand, show a unique pattern of relationship to the clinicopathologic outcomes of the disease. Patients with less than 1% (low) or more than 50% (high) expression of p21 by immunohistochemistry show a higher chance of metastasis, poor necrotic response to chemotherapy, and an overall decreased survival rate when compared with p21 expression between 1% and 50% (moderate). Our results also showed that the expression of p16 and combined p16 and p21 demonstrates a stronger predictive relationship to 5-year survival than tumor histologic necrosis and p21 alone. DISCUSSION: The results of this study, once proven to be reproducible by a larger number of patients, will be valuable in the initial assessment and risk stratification of the patients for treatment and possibly the clinical trials.

Clinical implications of DNA methylation-based integrated classification of histologically defined grade 2 meningiomas.

The combination of DNA methylation analysis with histopathological and genetic features allows for a more accurate risk stratification and classification of meningiomas. Nevertheless, the implications of this classification for patients with grade 2 meningiomas, a particularly heterogeneous tumor entity, are only partially understood. We correlate the outcomes of histopathologically confirmed grade 2 meningioma with an integrated molecular-morphologic risk stratification and determine its clinical implications. Grade 2 meningioma patients treated at our institution were re-classified using an integrated risk stratification involving DNA methylation array-based data, copy number assessment and TERT promoter mutation analyses. Grade 2 meningioma cases according to the WHO 2021 criteria treated between 2007 and 2021 (n = 100) were retrospectively analyzed. The median clinical and radiographic follow-up periods were 59.8 and 54.4 months. A total of 38 recurrences and 17 deaths were observed. The local control rates of the entire cohort after 2-, 4-, and 6-years were 84.3%, 68.5%, and 50.8%, with a median local control time of 77.2 months. The distribution of the integrated risk groups were as follows: 31 low, 54 intermediate, and 15 high risk cases. In the multivariable Cox regression analysis, integrated risk groups were significantly associated with the risk of local recurrence (hazard ratio (HR) intermediate: 9.91, HR high-risk: 7.29, p < 0.01). Gross total resections decreased the risk of local tumor progression (HR gross total resection: 0.19, p < 0.01). The comparison of 1p status and integrated risk groups (low vs. intermediate/high) revealed nearly identical local control rates within their respective subgroups. In summary, only around 50% of WHO 2021 grade 2 meningiomas have an intermediate risk profile. Integrated molecular risk stratification is crucial to guide the management of patients with grade 2 tumors and should be routinely applied to avoid over- and undertreatment, especially concerning the use of adjuvant radiotherapy.

Clinical analysis of 314 patients with high-grade squamous intraepithelial lesion who underwent total hysterectomy directly: a multi-center, retrospective cohort study.

OBJECTIVE: To identify the risk factors of cervical high-grade squamous intraepithelial lesion(HSIL) complicated with occult cervical cancer and standardize the management of initial treatment for HSIL. METHOD: The clinical data of patients who underwent total hysterectomy directly due to HSIL in the obstetrics and gynecology department of two tertiary hospitals and three secondary hospitals from 2018 to 2023 were collected. Their general characteristics, pathological parameters and survival status were analyzed. Logistic regression model was used to analyze the correlation between clinical parameters and postoperative pathological upgrading. RESULT: 1. Among the 314 patients with HSIL who underwent total hysterectomy directly, 73.2% were from primary hospitals. 2. 25 patients (7.9%) were pathologically upgraded to cervical cancer, all of which were early invasive cancer. 3. Up to now, there was no recurrence or death in the 25 patients with early-stage invasive cancer, and the median follow-up period was 21 months(range 2-59 months). 4. Glandular involvement(OR 3.968; 95%CI 1.244-12.662) and lesion range ≥ 3 quadrants (OR 6.527; 95% CI 1.78-23.931), HPV 16/18 infection (OR 5.382; 95%CI 1.947-14.872), TCT ≥ ASC-H (OR 4.719; 95%CI 1.892-11.766) were independent risk factors that affected the upgrading of postoperative pathology. 5. The area under the curve (AUC) calculated by the Logistic regression model was 0.840, indicating that the predictive value was good. CONCLUSION: There is a risk of occult cervical cancer in patients with HSIL. Glandular involvement, Lesion range ≥ 3 quadrants, HPV 16/18 infection and TCT ≥ ASC-H are independent risk factors for HSIL combined with occult cervical cancer. The prognosis of biopsy-proved HSIL patients who underwent extrafascial hysterectomy and unexpected early invasive cancer was later identified on specimen may be good.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Comparison of two region-of-interest placement methods for histogram analysis of apparent diffusion coefficient maps for glioma grading.

Objectives: We assessed the value of histogram analysis (HA) of apparent diffusion coefficient (ADC) maps for grading low-grade (LGG) and high-grade (HGG) gliomas. Methods: We compared the diagnostic performance of two region-of-interest (ROI) placement methods (ROI 1: the entire tumor; ROI 2: the tumor excluding cystic and necrotic portions). We retrospectively evaluated 54 patients with supratentorial gliomas (18 LGG and 36 HGG). All subjects underwent standard 3T contrast-enhanced magnetic resonance imaging. Histogram parameters of ADC maps calculated with the two segmentation methods comprised mean, median, maxi-mum, minimum, kurtosis, skewness, entropy, standard deviation (sd), mean of positive pixels (mpp), uniformity of positive pixels, and their ratios (r) between lesion and normal white matter. They were compared using the independent t-test, chi-square test, or Mann-Whitney U test. For statistically significant results, receiver operating characteristic curves were constructed, and the optimal cutoff value, sensitivity, and specificity were determined by maximizing Youden's index. Results: The ROI 1 method resulted in significantly higher rADC mean, rADC median, and rADC mpp for LGG than for HGG; these parameters had value for predicting the histological glioma grade with a cutoff (sensitivity, specificity) of 1.88 (77.8%, 61.1%), 2.25 (44.4%, 97.2%), and 1.88 (77.8%, 63.9%), respectively. The ROI 2 method resulted in significantly higher ADC mean, ADC median, ADC mpp, ADC sd, ADC max, rADC median, rADC mpp, rADC mean, rADC sd, and rADC max for LGG than for HGG, while skewness was lower for LGG than for HGG (0.27 [0.98] vs 0.91 [0.81], p = 0.014). In ROI 2, ADC median, ADC mpp, ADC mean, rADC median, rADC mpp, and rADC mean performed well in differentiating glioma grade with cutoffs (sensitivity, specificity) of 1.28 (77.8%, 88.9%), 1.28 (77.8%, 88.9%), 1.25 (77.8%, 91.7%), 1.81 (83.3%, 91.7%), 1.74 (83.3%, 91.7%), and 1.81 (83.3%, 91.7%), respectively. Conclusions: HA parameters had value for grading gliomas. Ex-cluding cystic and necrotic portions of the tumor for measuring HA parameters was preferable to using the entire tumor as the ROI. In this segmentation, rADC median showed the highest performance in predicting histological glioma grade, followed by rADC mpp, rADC mean, ADC median, ADC mpp, and ADC mean.