Predict Tumor Grade Research Articles

NIMG-01. RADIOMIC DATA ANALYSIS WITH ENSEMBLE MACHINE LEARNING TECHNIQUES PREDICT GRADE, HISTOPATHOLOGIC SUBTYPE, AND 1P/19Q CO-DELETION STATUS IN PATIENTS WITH LOW-GRADE GLIOMAS

Abstract Low-grade gliomas (LGGs) are a diverse group of primary brain tumors characterized by their variable prognosis and treatment response. Traditionally, the management of LGGs has relied on histopathological analysis, which sometimes offers limited insight into tumor behavior and treatment outcomes. Recent advances in radiomics have enabled the extraction of detailed quantitative features from routine imaging, providing a non-invasive method to assess tumor characteristics. These radiomic features can capture underlying tumor heterogeneity more comprehensively than conventional methods. Integrating radiomic data with ensemble machine learning techniques offers a promising approach to enhance the prediction of tumor grade, histopathologic subtype, and genetic alterations such as 1p/19q co-deletion status in LGGs, potentially leading to more tailored therapeutic strategies. We combined radiomic-genomic data from 159 magnetic resonance imaging (MRI) scans from the updated LGG-1p19qDeletion dataset (Erickson 2020) and 114 MRI scans from the ReMIND dataset (Juvekar 2024). Expert radiologists performed segmentations. A total of 2446 extracted radiomic features were initially extracted. Feature selection was performed using wrapper- and filter-based techniques with cross-validation. Prediction models were built using machine learning techniques, including random forest and extreme gradient boosting. Using Random Forest, we achieved high accuracy in predicting histopathologic subtype (88.14%, AUC 96.68%) with precision of 87.86% and Dice score of 87.88%. For grade prediction (accuracy 76.19%, AUC 91.84%), precision was 73.91%, and the Dice score was 77.27%. For 1p/19q co-deletion status (accuracy 75.61%, AUC 82.74%), precision was 77.78%, and the Dice score was 73.68%. Our study establishes a robust radiomic workflow to predict tumor grade, histopathologic subtype, and 1p/19q co-deletion status in low-grade gliomas, enhancing diagnostic accuracy and treatment personalization. This methodology can be applied clinically to improve the stratification of treatment plans for low-grade gliomas, potentially leading to effective, personalized therapy options. Using multicentric data potentially improved external validity, something which past research lacked. Future research should focus on validating and deploying this model using observational multicentric study designs.

Three-dimensional imaging of upper tract urothelial carcinoma improves diagnostic yield and accuracy.

Upper tract urothelial carcinoma (UTUC) is a rare form of urothelial cancer with a high incidence of recurrence and a low survival rate. Almost two-thirds of UTUCs are invasive at the time of diagnosis; therefore, improving diagnostic methods is key to increasing survival rates. Histopathological analysis of UTUC is essential for diagnosis and typically requires endoscopy biopsy, tissue sectioning, and labeling. However, endoscopy biopsies are minute, and it is challenging to cut into thin sections for conventional histopathology; this complicates diagnosis. Here, we used volumetric 3-dimensional (3D) imaging to explore the inner landscape of clinical UTUC biopsies, without sectioning, revealing that 3D analysis of phosphorylated ribosomal protein S6 (pS6) could predict tumor grade and prognosis with improved accuracy. By visualizing the tumor vasculature, we discovered that pS6+ cells were localized near blood vessels at significantly higher levels in high-grade tumors than in low-grade tumors. Furthermore, the clustering of pS6+ cells was associated with shorter relapse-free survival. Our results demonstrate that 3D volume imaging of the structural niches of pS6 cells deep inside the UTUC samples improved diagnostic yield, grading, and prognosis prediction.

Evaluation of contrast-enhanced ultrasound for predicting tumor grade in small (≤4 cm) clear cell renal cell carcinoma: Qualitative and quantitative analysis.

The study aimed to evaluate the utility of qualitative and quantitative analysis employing contrast-enhanced ultrasound (CEUS) in predicting the WHO/ISUP grade of small (≤4 cm) clear cell renal cell carcinoma (ccRCCs). Patients with small ccRCCs, confirmed by histological examination, underwent preoperative CEUS and were classified into low- (grade I/II) and high-grade (grade III/IV) groups. Qualitative and quantitative assessments of CEUS were conducted and compared between the two groups. Diagnostic performance was assessed using receiver operating characteristic curves. A total of 72 patients were diagnosed with small ccRCCs, comprising 23 individuals in the high-grade group and 49 in the low-grade group. The low-grade group exhibited a significantly greater percentage of hyper-enhancement compared to the high-grade group (79.6% VS 39.1%, P < 0.05). The low-grade group showed significantly higher relative index values for peak enhancement, wash-in area under the curve, wash-in rate, wash-in perfusion index, and wash-out rate compared to the high-grade group (all P < 0.05). The AUC values for qualitative and quantitative parameters in predicting the WHO/ISUP grade of small ccRCCs ranged from 0.676 to 0.756. Both qualitative and quantitative CEUS analysis could help to distinguish the high- from low-grade small ccRCCs.

A comprehensive evaluation of imaging features in pediatric spinal gliomas and their value in predicting tumor grade and histology

PurposePediatric spinal cord gliomas (PSGs) are rare in children and few reports detail their imaging features. We tested the association of tumoral grade with imaging features and proposed a novel approach to categorize post-contrast enhancement patterns in PSGs.MethodsThis single-center, retrospective study included patients <21 years of age with preoperative spinal MRI and confirmed pathological diagnosis of PSG from 2000-2022. Tumors were classified using the 5th edition of the WHO CNS Tumors Classification. Two radiologists reviewed multiple imaging features, and classified enhancement patterns using a novel approach. Fisher's exact test determined associations between imaging and histological features.ResultsForty-one PSGs were reviewed. Thirty-four were intramedullary, and seven were extramedullary. Pilocytic astrocytoma was the most common tumor (39.02%). Pain and weakness were the most prevalent symptoms. Seven patients (17.07%) died. Cyst, syringomyelia, and leptomeningeal enhancement were associated with tumor grade. Widening of the spinal canal was observed only in low-grade astrocytomas. There was a significant association between tumor grade and contrast enhancement pattern. Specifically, low-grade PSGs were more likely to exhibit type 1A enhancement (mass-like, with well-defined enhancing margins) and less likely to exhibit type 1B enhancement (mass-like, with ill-defined enhancing margins).ConclusionPSGs display overlapping imaging features, making grade differentiation challenging based solely on imaging. The correlation between tumor grade and contrast enhancement patterns suggests a potential diagnostic avenue, requiring further validation with larger, multicenter studies. Furthermore, Low-grade PSGs display cysts and syringomyelia more frequently, and leptomeningeal enhancement is less common.

Can the use of antithrombotic drugs be a predictive factor in the early diagnosis of bladder cancer?: A single-center analysis.

Hematuria is the most common symptom of bladder cancer (BCa). It is well-known that the frequency of hematuria increases with the use of antithrombotic drugs (ATDs). We designed our study with the hypothesis that patients using antithrombotic drugs who present with the complaint of hematuria and are subsequently diagnosed with BCa may receive an earlier diagnosis, leading to lower tumor grades and stages. Data of 441 consecutive patients who presented to our urology outpatient clinic with macroscopic hematuria between 2020 and 2023 were retrospectively evaluated. A total of 88 patients (21.4%) with a primary diagnosis of BCa were included in our study. Patients were divided into 2 groups: those using ATDs during the episode of macroscopic hematuria (group 1) and those not using ATDs (group 2). Univariate and multivariate binary logistic regression analysis was performed to identify risk factors that could predict tumor grade. The incidence of multiple tumors (>1) was significantly lower in patients using ATDs (P = .033). The number of patients with tumor size larger than 3 cm was significantly higher in the group not using ATDs (P = .005). The rates of pathological T1 stage in the group using ATDs were significantly lower than those in the nonuser group (P = .038). According to the results of the multivariate model, the effect of pathology stage and ATD use on predicting tumor grade was significant (P = .002 and P < .001, respectively). The probability of having a high-grade tumor in patients with pathology stage T1 was 5.32 times higher than in patients with pathology stage TA. The probability of having a high-grade tumor in patients not using ATDs was 7.73 times higher than in those using ATDs. The effect of pathology stage and ATD use on predicting tumor grade was found to be significant. The probability of having a high-grade tumor was higher in patients not using ATDs compared to those using ATDs. In light of these results, we can state that the use of ATDs is a positive predictive factor in the early diagnosis of BCa, bringing along the chance of early diagnosis and treatment.

Immune analysis of urine and plasma samples from patients with clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the third most common type of urological malignancy worldwide, and it is associated with a silent progression and late manifestation. Patients with a metastatic form of ccRCC have a poor prognosis; however, when the disease is diagnosed early, it is largely curable. Currently, there are no biomarkers available in clinical practice for ccRCC. Thus, the aim of the present study was to measure 27 biologically relevant cytokines in preoperative and postoperative urine samples, and in preoperative plasma samples from 34 patients with ccRCC, and to evaluate their diagnostic significance. The concentrations of cytokines were assessed by multiplex immune assay. The results showed significantly higher levels of IL-1 receptor antagonist, IL-6, IL-15, chemokine (C-C motif) ligand (CCL)2, CCL3, CCL4, C-X-C motif ligand (CXCL)10, granulocyte-macrophage colony stimulating factor (GM-CSF) and platelet-derived growth factor-BB (PDGF-BB), and lower levels of granulocyte colony stimulating factor (G-CSF) in urine samples from patients prior to surgery compared with those in the controls. Notably, the urine levels of G-CSF, IL-5 and vascular endothelial growth factor differed following tumor removal compared with the preoperative urine levels. In addition, urinary G-CSF, GM-CSF, IL-6, CXCL10, CCL5 and PDGF-BB appeared to be potential markers of tumor grade. Plasma from patients with ccRCC contained significantly higher levels of IL-6 and lower levels of CCL2 than control plasma. In conclusion, the present findings indicated that urinary and circulating cytokines may represent a promising novel tool for the early diagnosis of ccRCC and/or prediction of tumor grade.

Utilizing the amide proton transfer technique to characterize diffuse gliomas based on the WHO 2021 classification of CNS tumors.

Diffuse gliomas present a significant challenge for healthcare systems globally. While brain MRI plays a vital role in diagnosis, prognosis, and treatment monitoring, accurately characterizing gliomas using conventional MRI techniques alone is challenging. In this study, we explored the potential of utilizing the amide proton transfer (APT) technique to predict tumor grade and type based on the WHO 2021 Classification of CNS Tumors. Forty-two adult patients with histopathologically confirmed brain gliomas were included in the study. They underwent 3T MRI imaging, which involved APT sequence. Multinomial and binary logistic regression models were employed to classify patients into clinically relevant groups based on MRI findings and demographic variables. We found that the best model for tumor grade classification included patient age along with APT values. The highest sensitivity (88%) was observed for Grade 4 tumors, while Grade 3 tumors showed the highest specificity (79%). For tumor type classification, our model incorporated four predictors: APT values, patient's age, necrosis, and the presence of hemorrhage. The glioblastoma group had the highest sensitivity and specificity (87%), whereas balanced accuracy was the lowest for astrocytomas (0.73). The APT technique shows great potential for noninvasive evaluation of diffuse gliomas. The changes in the classification of gliomas as per the WHO 2021 version of the CNS Tumor Classification did not affect its usefulness in predicting tumor grade or type.

Transcranial Magnetic Stimulation-Based Machine Learning Prediction of Tumor Grading in Motor-Eloquent Gliomas.

Navigated transcranial magnetic stimulation (nTMS) is a well-established preoperative mapping tool for motor-eloquent glioma surgery. Machine learning (ML) and nTMS may improve clinical outcome prediction and histological correlation. This was a retrospective cohort study of patients who underwent surgery for motor-eloquent gliomas between 2018 and 2022. Ten healthy subjects were included. Preoperative nTMS-derived variables were collected: resting motor threshold (RMT), interhemispheric RMT ratio (iRMTr)-abnormal if above 10%-and cortical excitability score-number of abnormal iRMTrs. World Health Organization (WHO) grade and molecular profile were collected to characterize each tumor. ML models were fitted to the data after statistical feature selection to predict tumor grade. A total of 177 patients were recruited: WHO grade 2-32 patients, WHO grade 3-65 patients, and WHO grade 4-80 patients. For the upper limb, abnormal iRMTr were identified in 22.7% of WHO grade 2, 62.5% of WHO grade 3, and 75.4% of WHO grade 4 patients. For the lower limb, iRMTr was abnormal in 23.1% of WHO grade 2, 67.6% of WHO grade 3%, and 63.6% of WHO grade 4 patients. Cortical excitability score ( P = .04) was statistically significantly related with WHO grading. Using these variables as predictors, the ML model had an accuracy of 0.57 to predict WHO grade 4 lesions. In subgroup analysis of high-grade gliomas vs low-grade gliomas, the accuracy for high-grade gliomas prediction increased to 0.83. The inclusion of molecular data into the model-IDH mutation and 1p19q codeletion status-increases the accuracy of the model in predicting tumor grading (0.95 and 0.74, respectively). ML algorithms based on nTMS-derived interhemispheric excitability assessment provide accurate predictions of HGGs affecting the motor pathway. Their accuracy is further increased when molecular data are fitted onto the model paving the way for a joint preoperative approach with radiogenomics.

3D MR elastography-based stiffness as a marker for predicting tumor grade and subtype in cervical cancer

BackgroundIncreasing evidence has indicated that high tissue stiffness (TS) may be a potential biomarker for evaluation of tumor aggressiveness. PurposeTo investigate the value of magnetic resonance elastography (MRE)-based quantitative parameters preoperatively predicting the tumor grade and subtype of cervical cancer (CC). Study typeRetrospective. PopulationTwenty-five histopathology-proven CC patients and 7 healthy participants. Field strength/sequence3.0T, magnetic resonance imaging (MRI) (LAVA-flex) and MRE with a three-dimensional spin-echo echo-planar imaging. AssessmentThe regions of interest (ROIs) were manually drawn by two observers in tumors to measure mean TS, storage modulus (G′), loss modulus (G″) and damping ratio (DR) values. Surgical specimens were evaluated for tumor grades and subtypes. Statistical testsIntraclass correlation coefficient (ICC) was expressed in terms of inter-observer agreements. t-test or Mann-Whitney nonparametric test was used to compare the complex modulus and apparent diffusion coefficient (ADC) values between different tumor groups. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the diagnostic performance. ResultsThe TS of endocervical adenocarcinoma (ECA) group was significantly higher than that in squamous cell carcinoma (SCC) group (5.27 kPa vs. 3.44 kPa, P = 0.042). The TS also showed significant difference between poorly and well/moderately differentiated CC (5.21 kPa vs. 3.47 kPa, P = 0.038), CC patients and healthy participants (4.18 kPa vs. 1.99 kPa, P < 0.001). The cutoff value of TS to discriminate ECA from SCC was 4.10 kPa (AUC: 0.80), while it was 4.42 kPa to discriminate poorly from well/moderately differentiated CC (AUC: 0.83), and 2.25 kPa to distinguish normal cervix from CC (AUC: 0.88), respectively. There were no significant difference in G″, DR and ADC values between any subgroups except for comparison of healthy participants and CC patients (P = 0.001, P = 0.004, P < 0.001, respectively). Data conclusion3D MRE-assessed TS shows promise as a potential biomarker to preoperatively assess tumor grade and subtype of CC.

Ki-67 Proliferation Index Is Associated With Tumor Grade and Survival in Pleural Epithelioid Mesotheliomas.

Pleural epithelioid mesothelioma (PEM) is divided into low and high grades based on nuclear atypia, mitoses, and necrosis in the tumor. Assessing mitoses and nuclear atypia tend to be labor-intensive with limited reproducibility. Ki-67 proliferation index was shown to be a prognostic factor in PEM, but its performance has not been directly correlated with tumor grade or mitotic score. This study evaluated the potential of Ki-67 index as a surrogate of tumor grade. We also compared the predictability of mitoses and Ki-67 index for overall survival (OS). Ninety-six PEM samples from 85 patients were identified from the surgical pathology file during 2000-2021 at our institution, and all glass slides were reviewed by 2 pulmonary pathologists to confirm the diagnosis and assign the tumor grade. Digital image analysis (DIA) was done for Ki-67 index. The agreement on tumor grading between 2 reviewers was moderate (kappa value = 0.47). The correlation between mitotic count (average count by 2 reviewers) and Ki-67 index was 0.65. The areas under the curve for predicting tumor grade by mitotic score and Ki-67 index were 0.84 and 0.74 (reviewer 1) and 0.85 and 0.81 (reviewer 2), respectively. High Ki-67 index and mitoses were significantly associated with poor OS ( P =0.03 and 0.0005, using 30% and 10/2mm 2 as cutoffs, respectively). In conclusion, Ki-67 index by DIA was associated with tumor grade as well as mitotic count, and its predictability for OS was comparable to that of mitotic score, thus being a potential surrogate for tumor grade.

The role of preoperative [11C]methionine PET in defining tumor-related epilepsy and predicting short-term postoperative seizure control in temporal lobe low-grade gliomas.

Surgery is the mainstay of treatment for low-grade glioma (LGG)-related epilepsy. However, the goal of achieving both oncological radical resection and seizure freedom can be challenging. PET with [11C]methionine (MET) has been recently introduced in clinical practice for the management of patients with LGGs, not only to monitor the response to treatments, but also as a preoperative tool to define the metabolic tumor extent and to predict tumor grading, type, and prognosis. Still, its role in defining tumor-related epilepsy and postoperative seizure outcomes is limited. The aim of this preliminary study was to investigate the role of MET PET in defining preoperative seizure characteristics and short-term postoperative seizure control in a cohort of patients with newly diagnosed temporal lobe low-grade gliomas (tLGGs). Patients with newly diagnosed and histologically proven temporal lobe grade 2/3 gliomas (2021 WHO CNS tumor classification) who underwent resection at the authors' institution between July 2011 and March 2021 were included in this retrospective study. MET PET images were acquired, fused with MRI scans, and qualitatively and semiquantitatively analyzed. Any eventual PET/MRI involvement of the temporomesial area, seizure characteristics, and 1-year seizure outcomes were reported. A total of 52 patients with tLGGs met the inclusion criteria. MET PET was positive in 41 (79%) patients, with a median metabolic tumor volume of 14.56 cm3 (interquartile range [IQR] 6.5-28.2 cm3). The median maximum and mean tumor-to-background ratio (TBRmax, TBRmean) were 2.24 (IQR 1.58-2.86) and 1.53 (IQR 1.37-1.70), respectively. The metabolic tumor volume was found to be related to the presence of seizures at disease onset, but only in noncodeleted tumors (p = 0.014). Regarding patients with uncontrolled seizures at surgery, only the temporomesial area PET involvement showed a statistical correlation both in the univariate (p = 0.058) and in the multivariate analysis (p = 0.030). At 1-year follow-up, seizure control was correlated with MET PET-derived semiquantitative data. Particularly, higher TBRmax (p = 0.0192) and TBRmean (p = 0.0128) values were statistically related to uncontrolled seizures 1 year after surgery. This preliminary study suggests that MET PET may be used as a preoperative tool to define seizure characteristics and outcomes in patients with tLGGs. These findings need to be further validated in larger series with longer epileptological follow-ups.

Improved diagnostic confidence and tumor type prediction in adult-type diffuse glioma by multimodal imaging including DCE perfusion and diffusion kurtosis mapping – A standardized multicenter study

Background and purposeTo evaluate the feasibility of a multimodal approach involving dynamic contrast-enhanced (DCE) perfusion imaging and diffusion kurtosis imaging (DKI) in the preoperative imaging of brain tumors in a multicenter setting, and to evaluate the effect on diagnostic confidence and accuracy for tumor grade and type prediction. Materials and MethodsOne hundred and thirty-three patients with brain tumors were imaged in six hospitals with a standardized multimodal protocol. Standard imaging and six parameter maps derived from DCE and DKI sequences were reviewed off-site by two independent readers. Image quality and diagnostic confidence were evaluated in qualitative analyses. Quantitative analyses were performed to assess diagnostic accuracy and the performance of DKI and DCE parameters for tumor grade differentiation and molecular tumor type determination. ResultsStandardized acquisition of DCE and DKI maps was feasible with excellent image quality. Diagnostic confidence was significantly improved from 85 % to 96 % (p = 0.0005) by additional review of the DCE and DKI maps. The combination of mean kurtosis and CBV was particularly advantageous for differentiating low-grade and high-grade glioma, oligodendroglial vs. astrocytic, and IDH1/2 wild type vs. mutated tumors. ConclusionA multimodal imaging approach with DCE and DKI improves diagnostic confidence and yields higher diagnostic accuracy for predicting tumor grade and type in adult-type glioma.

The DDD score outperforms the RENAL score in predicting high-grade renal cell carcinoma.

To explore the relationship between Fuhrman grade of renal cell carcinoma (RCC) and the DDD score. We reviewed the records of 527 nonmetastatic RCC patients. Demographic, clinical, and pathologic characteristics were reviewed. Binary logistic regression was used to explore the independent risk factors for high-grade RCC (HGRCC). Sex, BMI (Body Mass Index), RNS, and DDD score were significantly correlated with HGRCC. Based on these independent risk factors, we constructed two predictive models integrating the RNS and DDD scores with sex and BMI to predict tumor grade. The calibration curves of the predictive model showed good agreement between the observations and predictions. The concordance indexes (C-indexes) of the predictive models were 0.768 (95% CI, 0.713-0.824), and 0.809 (95% CI, 0.759-0.859). Receiver operating characteristic (ROC) curves were performed to compare the predictive power of the nomograms, and the prediction model including the DDD score had better prognostic ability (p = 0.01). This study found that RNS, DDD score, BMI, and sex were independent predictors of HGRCC. We developed effective nomograms integrating the above risk factors to predict HGRCC. Of note, the nomogram including the DDD score achieves better prediction ability for HGRCC.

Dual-layer spectral detector CT: A noninvasive preoperative tool for predicting histopathological differentiation in pancreatic ductal adenocarcinoma

PurposeTo predict histopathological differentiation grades in patients with pancreatic ductal adenocarcinoma (PDAC) before surgery with quantitative and qualitative variables obtained from dual-layer spectral detector CT (DLCT). MethodsTotally 128 patients with histopathologically confirmed PDAC and preoperative DLCT were retrospectively enrolled and categorized into the low-grade (LG) (well and moderately differentiated, n = 82) and high-grade (HG) (poorly differentiated, n = 46) subgroups. Both conventional and spectral variables for PDAC were measured. The ratio of iodine concentration (IC) values in arterial phase(AP) and venous phase (VP) was defined as iodine enhancement fraction_AP/VP (IEF_AP/VP). Necrosis was visually assessed on both conventional CT images (necrosis_con) and virtual mono-energetic images (VMIs) at 40 keV (necrosis_40keV). Forward stepwise logistic regression method was conducted to perform univariable and multivariable analysis. Receiver operating characteristic (ROC) curves and the DeLong method were used to evaluate and compare the efficiencies of variables in predicting tumor grade. ResultsNecrosis_con (odds ratio [OR] = 2.84, 95% confidence interval [CI]: 1.13–7.13; p < 0.001) was an independent predictor among conventional variables, and necrosis_40keV (OR = 5.82, 95% CI: 1.98–17.11; p = 0.001) and IEF_AP/VP (OR = 1.12, 95% CI:1.07–1.17; p < 0.001) were independent predictors among spectral variables for distinguishing LG PDAC from HG PDAC. IEF_AP/VP (AUC = 0.754, p = 0.016) and combination model (AUC = 0.812, p < 0.001) had better predictive performances than necrosis_con (AUC = 0.580). The combination model yielded the highest sensitivity (72%) and accuracy (79%), while IEF_AP/VP exhibited the highest specificity (89%). ConclusionVariables derived from DLCT have the potential to preoperatively evaluate PDAC tumor grade. Furthermore, spectral variables and their combination exhibited superior predictive performances than conventional CT variables.

Current role of radiomics and radiogenomics in predicting oncological outcomes in bladder cancer

Abstract Background Radiomics refers to the conversion of medical images into high-throughput, quantifiable data to analyze disease patterns, aid decision-making, and predict prognosis. Radiogenomics is an extension of radiomics and involves a combination of conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data. In the field of bladder cancer, studies have investigated the development, implementation, and efficacy of radiomic and radiogenomic nomograms in predicting tumor grade, gene expression, and oncological outcomes, with variable results. We aimed to perform a systematic review of the current literature to investigate the development of a radiomics-based nomogram to predict oncological outcomes in bladder cancer. Materials and methods The Medline, EMBASE, and Web of Science databases were searched up to February 17, 2023. Gray literature was also searched to further identify other suitable publications. Quality assessment of the included studies was performed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score. Results Radiogenomic nomograms generally had good performance in predicting the primary outcome across the included studies. The median area under the curve, sensitivity, and specificity across the included studies were 0.83 (0.63–0.973), 0.813, and 0.815, respectively, in the training set and 0.75 (0.702–0.838), 0.723, and 0.652, respectively, in the validation set. Conclusions Several studies have demonstrated the predictive potential of radiomic and radiogenomic models in advanced pelvic oncology. Further large-scale studies in a prospective setting are required to further validate results and allow generalized use in modern medicine.

Combined expression of JHDM1D/KDM7A gene and long non-coding RNA RP11-363E7.4 as a biomarker for urothelial cancer prognosis.

Bladder cancer is the tenth most frequently diagnosed cancer globally. Classification of high- or low-grade tumors is based on cytological differentiation and is an important prognostic factor. LncRNAs regulate gene expression and play critical roles in the occurrence and development of cancer, however, there are few reports on their diagnostic value and co-expression levels with genes, which may be useful as specific biomarkers for prognosis and therapy in bladder cancer. Thus, we performed a marker lesion study to investigate whether gene/lncRNA expression in urothelial carcinoma tissues may be useful in differentiating low-grade and high-grade tumors. RT-qPCR was used to evaluate the expression of the JHDM1D gene and the lncRNAs CTD-2132N18.2, SBF2-AS1, RP11-977B10.2, CTD-2510F5.4, and RP11-363E7.4 in 20 histologically diagnosed high-grade and 10 low-grade tumors. A protein-to-protein interaction network between genes associated with JHDM1D gene was constructed using STRING website. The results showed a moderate (positive) correlation between CTD-2510F5.4 and CTD2132N18.2. ROC curve analyses showed that combined JHDM1D and RP11-363E7.4 predicted tumor grade with an AUC of 0.826, showing excellent accuracy. In conclusion, the results indicated that the combined expression of JHDM1D and RP11-363E7.4 may be a prognostic biomarker and a promising target for urothelial tumor therapy.

Prognostic value of hematological indexes in endometrioid type endometrial cancer

Endometrial cancer remains a significant health concern, particularly in developed nations where its prevalence is increasing. Reliable prognostic markers are paramount to enhance disease management and patient outcomes. This study explores the prognostic capabilities of various hematological indices, including the eosinophil-lymphocyte ratio (ELR) and the prognostic nutritional index (PNI), in relation to endometrioid type endometrial cancer (ETEC) staging and grading. We retrospectively analyzed data from ETEC patients, categorizing them by tumor grade and stage, and evaluating their hematological indices from preoperative blood samples. Our results indicated that higher ELR and lower PNI levels correlate with more advanced tumor grades and stages. Additionally, the platelet-lymphocyte ratio (PLR) was found to be the most significant marker for staging, with PLT, SII, SIRI, ELR, and PNI also showing significant differences across stages. These findings suggest that hematological indices such as ELR and PNI could be instrumental in predicting tumor grade and stage, offering valuable insights for clinical practice. Future research should focus on prospective studies to confirm these findings and consider the integration of these markers into standard diagnostic protocols.

Machine Learning and Radiomics in Gliomas.

The integration of machine learning (ML) and radiomics is emerging as a pivotal advancement in glioma research, offering novel insights into the diagnosis, prognosis, and treatment of these complex tumors. Radiomics involves the extraction of a multitude of quantitative features from medical images. When these features are analyzed through ML algorithms, the precision of tumor characterization is enhanced beyond traditional methods.This chapter examines the application of both supervised and unsupervised ML techniques for interpreting radiomic data, highlighting their potential for accurately predicting tumor grade, identifying genetic mutations, estimating patient survival rates, and evaluating treatment responses. The ability of ML-based radiomic analysis to discern intricate patterns in tumor imaging, imperceptible to human observation, is particularly emphasized.Challenges in this field, including data diversity, overfitting risks, and the need for extensive, annotated datasets, are critically assessed. The necessity of integrating these advanced technologies into clinical practice through interdisciplinary collaboration is underscored as a crucial factor for their effective utilization.Overall, the synergy between ML and radiomics in glioma research represents a significant step toward personalized medicine, offering enhanced tools for patient-specific treatment strategies.

Survival and grade of the glioma prediction using transfer learning.

Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3-6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients.

Added-value of 3D amide proton transfer MRI in assessing prognostic factors of cervical cancer: a comparative study with multiple model diffusion-weighted imaging.

Amide proton transfer (APT) imaging has been gradually applied to cervical cancer, yet the relationships between APT and multiple model diffusion-weighted imaging (DWI) have yet to be investigated. This study attempted to evaluate the added value of 3-dimensional (3D) APT imaging to multiple model DWI for assessing prognostic factors of cervical cancer. This prospective diagnostic study was conducted in The First Affiliated Hospital of Zhengzhou University. A total of 88 consecutive patients with cervical cancer underwent APT imaging and DWI with 11 b-values (0-2,000 s/mm2). The apparent diffusion coefficient (ADC), pure molecular diffusion (D), perfusion fraction (f), pseudo-diffusion (D*), mean kurtosis (MK), and mean diffusivity (MD) were calculated based on mono-exponential, bi-exponential, and kurtosis models. The mean, minimum, and maximum values of APT signal intensity (APT SI) and DWI-derived metrics were compared based on tumor stages, subtypes, grades, and lymphovascular space invasion status by Student's t-test or Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the parameters. APT SImax, APT SImin, MKmean, and MKmax showed significant differences between adenocarcinoma (AC) and squamous cell carcinoma (SCC) (all P<0.05). APT SImean, APT SImax, and MKmax were higher and ADCmin, Dmean, Dmin, and MDmin were lower in the high-grade tumor than in low-grade tumor (all P<0.05). For distinguishing lymphovascular space invasion, only MKmean showed significant difference (P=0.010). APT SImax [odds ratio (OR) =2.347, P=0.029], APT SImin (OR =0.352; P=0.024), and MKmean (OR =6.523; P=0.001) were the independent predictors for tumor subtype, and APT SImax (OR =2.885; P=0.044), MDmin (OR =0.155, P=0.012) were the independent predictors for histological grade of cervical cancer. When APT SImin and APT SImax was combined with MKmean and MKmax, the diagnostic performance was significantly improved for differentiating AC and AC [area under the curve (AUC): 0.908, sensitivity: 87.5%; specificity: 83.3%; P<0.001]. The combination of APT SImean, APT SImax, ADCmin, MKmax, and MDmin demonstrated the highest diagnostic performance for predicting tumor grade (AUC: 0.903, sensitivity: 78.6%; specificity: 88.9%; P<0.001). Addition of APT to DWI may improve the ability to noninvasively predict poor prognostic factors of cervical cancer.