Dynamic interplay between corticosteroid treatment and the role of SRC-1 gene dysregulation in the progression of WHO-Grade 4 Astrocytoma.

BACKGROUND: Corticosteroid is commonly used before surgery to control cerebral oedema in brain tumours and is frequently continued throughout treatment. Its long-term effect of on the recurrence of WHO-Grade 4 astrocytoma remains controversial. The interaction between corticosteroid, SRC-1 gene and cytotoxic T-cells has never been investigated. METHODS: A retrospective cohort of 36 patients with WHO-Grade 4 astrocytoma were examined for CD8 + T-cell and SRC-1 gene expressions through IHC and qRT-PCR. The impact of corticosteroid on CD8+T-cells infiltration, SRC-1 expression, and tumour recurrence was analyzed. RESULTS: The mean patients age was 47-years, with a male to female ratio 1.2. About 78% [n = 28] of the cases showed reduced or no CD8+T-cell expression while 22% [n = 8] of cases have showed medium to high CD8+T-cell expression. SRC-1 gene was upregulated in 5 cases [14%] and 31 cases [86%] showed SRC-1 downregulation. The average of total days and doses of administered corticosteroid from the preoperative period to the postoperative period was at range of 14-106 days and 41-5028 mg, respectively. There was no significant statistical difference in RFI among tumours expressing high or low CD8+T-cells when corticosteroid was administered in recommended or exceeded doses [p-value = 0.640]. There was a significant statistical difference in RFI between CD8+T-Cell expression and SRC-1 gene dysregulation [p-value = 002]. Tumours with high CD8+T T-cell expression and SRC-1 gene downregulation had late recurrence. CONCLUSIONS: Corticosteroid treatment can directly affect the SRC-1 gene regulation but does not directly influence cytotoxic T-cells infiltration or tumor progression. However, SRC-1 gene downregulation can facilitate late tumor recurrence.

Future artificial intelligence tools and perspectives in medicine.

PURPOSE OF REVIEW: Artificial intelligence has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular data, clinical variables, etc.) and used the statistical and machine-learning methods to measure the model performance. In this review, we summarized and discussed the most recent radiomic pipeline used for clinical analysis. RECENT FINDINGS: Currently, limited management of cancers benefits from artificial intelligence, mostly related to a computer-aided diagnosis that avoids a biopsy analysis that presents additional risks and costs. Most artificial intelligence tools are based on imaging features, known as radiomic analysis that can be refined into predictive models in noninvasively acquired imaging data. This review explores the progress of artificial intelligence-based radiomic tools for clinical applications with a brief description of necessary technical steps. Explaining new radiomic approaches based on deep-learning techniques will explain how the new radiomic models (deep radiomic analysis) can benefit from deep convolutional neural networks and be applied on limited data sets. SUMMARY: To consider the radiomic algorithms, further investigations are recommended to involve deep learning in radiomic models with additional validation steps on various cancer types.

Evaluating the Effects of Prostate Radiotherapy Intensified with Pelvic Nodal Radiotherapy and Androgen Deprivation Therapy on Myelosuppression: Single-Institution Experience.

BACKGROUND: Prostate cancer (PCa) management commonly involves the utilization of prostate radiotherapy (PRT), pelvic nodal radiotherapy (PNRT), and androgen deprivation therapy (ADT). However, the potential association of these treatment modalities with bone marrow (BM) suppression remains inadequately reported in the existing literature. This study is designed to comprehensively evaluate the risk of myelosuppression associated with PRT, shedding light on an aspect that has been underrepresented in prior research. MATERIALS AND METHODS: We conducted a retrospective analysis of 600 patients with prostate cancer (PCa) treated with prostate radiotherapy (PRT) at a single oncology center between 2007 and 2017. Patients were categorized into four cohorts: PRT alone (n = 149), PRT + ADT, (n = 91), PRT + PNRT (n = 39), and PRT + PNRT + ADT (n = 321). To assess the risk of myelosuppression, we scrutinized specific blood parameters, such as hemoglobin (HGB), white blood cells (WBCs), neutrophils (NEUT), lymphocytes (LYM), and platelets (PLT) at baseline, mid-treatment (mRT), immediately post-RT (pRT), 1 month post-RT (1M-pRT), and 1 year post-RT (1Y-pRT). The inter-cohort statistical significance was evaluated with further stratification based on the utilized RT technique {3D conformal radiotherapy (3D-CRT), and intensity-modulated radiation therapy (IMRT)}. RESULTS: Significant statistical differences at baseline were observed in HGB and LYM values among all cohorts (p < 0.05). Patients in the PRT + PNRT + ADT cohort had significantly lower HGB at baseline and 1M-pRT. In patients undergoing ADT, BMS had a significant impact at 1M-pRT {odds ratio (OR) 9.1; 95% Confidence Interval (CI) 4.8-17.1} and at 1Y-pRT (OR 2.84; CI 1.14-7.08). The use of 3D-CRT was linked to reduced HGB levels in the PRT + PNRT + ADT group at 1 month pRT (p = 0.015). Similarly, PNRT significantly impacted BMS at 1M-pRT (OR 6.7; CI 2.6-17.2). PNRT increased the odds of decreased WBC counts at 1Y-pRT (OR 6.83; CI: 1.02-45.82). Treatment with any RT techniques (3D-CRT or IMRT), particularly in the PRT + PNRT and PRT + PNRT + ADT groups, significantly increased the odds of low LYM counts at all time points except immediately pRT (p < 0.05). Furthermore, NEUT counts were considerably lower at 1M-pRT (p < 0.05) in the PRT + PNRT + ADT group. PLT counts were significantly decreased by PRT + PNRT + ADT at mRT (OR 2.57; 95% CI: 1.42-4.66) but were not significantly impacted by the RT technique. CONCLUSIONS: Treatment with PRT, ADT, PNRT, and 3D-CRT is associated with BMS. Despite this statistically significant risk, no patient required additional interventions to manage the outcome. While its clinical impact appears limited, its importance cannot be underestimated in the context of increased integration of novel systemic agents with myelosuppressive properties. Longer follow-up should be considered in future studies.

The effect of p53 on the activity of NRF2 and NDRG2 genes through apoptotic pathway in IDH-wildtype glioblastoma.

BACKGROUND: Tumor suppressor (p53) acts to integrate multiple stress signals into diverse antiproliferative responses. Its potential to transactivate or downregulate genes through apoptotic pathway in IDH-wildtype glioblastoma has never been explored. METHODS: A group of twenty patients diagnosed with IDH-wildtype glioblastoma, were tested for p53 expression and NDRG2/NRF2 genes activity through protein and gene profiling assays. The connotation between these elements has been explored. RESULTS: The mean patients' age was 64-years. All tumors were IDH-wildtype. p53 was expressed in 12 tumors and absent in 8 tumors. The activity of NDRG2 gene was downregulated in all cases. The activity of NRF2 gene was upregulated in 17 tumors and downregulated in 3 tumors. There was a significant statistical difference in PFS among tumors exhibiting different levels of p53 expression and NDRG2 gene activity [p-value= 0.025], in which 12 tumors with downregulated NDRG2 expression and positive p53 expression had earlier tumor recurrence. This statistical difference in PFS was insignificant when we compared p53 expression with NRF2 gene activity [p-value= 0.079]. CONCLUSIONS: During cell cycle arrest at G2 phase, p53 expression in IDH-wildtype glioblastoma in elderly individuals, coupled with the downregulation of NDRG2 gene activity, led to an aberrant increase in tumor cell proliferation and accelerated tumor recurrence. However, the influence of p53 on NRF2 gene activity was found to be insignificant.

Deep Survival Analysis With Clinical Variables for COVID-19.

OBJECTIVE: Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients. METHODS AND PROCEDURES: We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups. RESULTS: Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19. CONCLUSION: Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner. CLINICAL IMPACT: The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects.

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

The Rate of Prostatic Involvement in Men Treated With Radical Cystectomy for Muscle Invasive Bladder Cancer.

PURPOSE: Radical cystoprostatectomy (RC) is one standard treatment for muscle-invasive bladder cancer (MIBC) in male patients. Another therapeutic option is trimodal therapy. Including the prostate in the trimodal therapy radiation therapy volume is based on MIBC surgical series showing prostatic stromal (PS) involvement. Our aim was to establish the rate of pathologic PS involvement by preoperative T stage in men treated with RC for MIBC. METHODS AND MATERIALS: We conducted a retrospective review of men with MIBC treated with RC between 2006 and 2019. Electronic medical records were reviewed, and preoperative clinical staging data were collected. χ2 test was done to test for a statistically significant difference in the rate of prostatic involvement between clinical tumor (cT) stages. Preoperatively identified carcinoma in situ, lymph node involvement, tumor location, and urethral involvement were also analyzed to see if they conferred a higher risk of PS involvement. Multivariate analysis using multiple logistic regression was performed. RESULTS: We identified 283 men with bladder cancer treated with RC. Patients with non-MIBC or incomplete medical data were excluded (n = 72). We analyzed 211 patients, and 46 (22%) had pathologic PS involvement. PS involvement by preoperative T stage was cT2 = 18%, cT3 = 23%, and cT4 = 48%. Twenty-nine (12%) patients had clinical lymph node involvement, of whom 19 (76%) had PS involvement. Thirty-four (16%) had urethral involvement, of whom 17 (50%) had PS involvement. Sixteen percent and 17% of percent of clinical T2 and T3 patients had bladder neck/trigone tumors, of whom 57% and 50% had prostatic involvement. Clinical T2 and T3 were not statistically different with regards to PS involvement (P = .385). Preoperative urethral involvement, lymph node involvement, cT4, and bladder neck/trigone location were statistically significant predictors of pathologic PS involvement (all P < .05). On multivariate analysis, only clinical urethral involvement was significant (P < .0001). CONCLUSIONS: The high rate of pathologic PS involvement seen in cT2 patients and the lack of ability of cT stage to predict PS involvement support routinely treating the prostate in trimodal therapy. Patients with preoperatively identified bladder neck/trigone tumors, urethral involvement, positive lymph nodes, or prostatic involvement are a subset at even higher risk of having pathologic PS involvement.

The Cancer Driver Genes IDH1 and IDH2 and CD204 in WHO-Grade 4 Astrocytoma: Crosstalk Between Cancer Metabolism and Tumour Associated Macrophage Recruitment in Tumour Microenvironment.

Purpose: IDH1 and IDH2 are hotspot mutations commonly identified in WHO-grade 4 astrocytomas. Their association with TAMs has never been investigated. We aim to explore the crosstalk between the IDH1/2 mutation metabolic effect and TAMs in tumour microenvironment and how this relationship affects the tumour recurrence. Patients and Methods: The study included 20 samples of patients with WHO-grade 4 astrocytoma. The alteration hotspot in codon IDH1R132 and IDH2R172 was examined using direct sequencing. The protein expression of CD204 on TAM was detected through immunohistochemistry. Results: IDH1R132 and IDH2R172 were symmetrically identified as wildtype in 18/20 tumours (90%) and the remaining 2 tumours (10%) showed synonymous mutations on both codons. Tumours with IDH1/2-wildtype showed high expression of CD204+TAMs in 10 cases and low expression in 8 cases. Typical expression was seen equally in IDH1/2 mutant tumours. There was no significant association between IDH1/2 and CD204+TAM expression (p= 0.999). The association between the two groups was significantly observed among IDH-wildtype tumours (p=0.027). Highly expressed CD204 in IDH-wildtype tumours showed a median recurrence at 10 months compared to low CD204 expression, showed a median recurrence interval at 24 months. Conclusion: IDH1R132 or IDHR172 has the same impact on the classification and prognosis of WHO-grade 4 astrocytoma. There was no crosstalk between IDH1/2 metabolic effect and CD204+TAM. However, IDH-wildtype glioblastomas with dense CD204+TAM are associated with early recurrence. Because the sample size is small, a larger study is recommended to determine the impact of IDH1/2 on TAMs.

Major Histocompatibility Class-I (MHC-I) downregulation in glioblastoma is a poor prognostic factor but not a predictive indicator for treatment failure.

BACKGROUND: MHC-I expression is a crucial factor in cancer immunity, and its regulations can impact tumor progression and recurrence. The mechanism through which glioblastoma use MHC-I to avoid immunosurveillance has been rarely investigated. METHODS: A retrospective cohort of 35 patients with IDH-mutant WHO-Grade 4 astrocytoma and IDH-wildtype glioblastoma were examined for MHC-I using protein and gene expression assays. The association between IDH mutation, TP53 mutation, and MHC-I expression with recurrence-free interval were investigated. RESULTS: The average patients' age was 49.6 year. IDH was wildtype in 13 tumors. MHC-I protein expression was absent in 30 tumors, faint in 4 tumors, and membrane bound dense expression in single tumor. MHC-I expression was upregulated in 10 tumors and 25 tumors showed MHC-I downregulation. P53 was positively expressed in 19 cases and lost in 13 cases. A significant statistical difference was observed in the RFI between tumors with distinct MHC-I expression and IDH-mutation [p-value = 0.008]. IDH-wildtype tumors with upregulated MHC-I expression showed late tumor recurrence compared to IDH-wildtype tumors with downregulated MHC-I expression. There was insignificant statistical difference in RFI among patients with varying degree of MHC-I expression, who received TMZ or TMZ and other chemotherapies [P-value = 0.44] CONCLUSIONS: Glioblastoma with upregulated MHC-I showed a delayed tumor recurrence in comparison to those with downregulated MHC-I expression. However, downregulated MHC-I may not necessarily be an indicator of poor problems.

Simple approach for the histomolecular diagnosis of central nervous system gliomas based on 2021 World Health Organization Classification.

The classification of central nervous system (CNS) glioma went through a sequence of developments, between 2006 and 2021, started with only histological approach then has been aided with a major emphasis on molecular signatures in the 4th and 5th editions of the World Health Organization (WHO). The recent reformation in the 5th edition of the WHO classification has focused more on the molecularly defined entities with better characterized natural histories as well as new tumor types and subtypes in the adult and pediatric populations. These new subclassified entities have been incorporated in the 5th edition after the continuous exploration of new genomic, epigenomic and transcriptomic discovery. Indeed, the current guidelines of 2021 WHO classification of CNS tumors and European Association of Neuro-Oncology (EANO) exploited the molecular signatures in the diagnostic approach of CNS gliomas. Our current review presents a practical diagnostic approach for diffuse CNS gliomas and circumscribed astrocytomas using histomolecular criteria adopted by the recent WHO classification. We also describe the treatment strategies for these tumors based on EANO guidelines.