Virtual Biopsy: Just an AI Software or a Medical Procedure?

The additional value of 68Ga-PSMA PET/CT SUVmax in predicting ISUP GG ≥ 2 and ISUP GG ≥ 3 prostate cancer in biopsy

BACKGROUND

Prebiopsy magnetic resonance imaging (MRI) increases the detection rate of clinically significant prostate cancer (csPCa). Prostate-specific membrane antigen-positron emission tomography/computed tomography (PSMA PET/CT) maximum standardized uptake value (SUVmax) of the prostate may offer additional value in predicting the likelihood of csPCa in biopsy.

METHODS

A single-center cohort study involving patients with biopsy-proven PCa who underwent both MRI and PSMA PET/CT between 2020 and 2021. Logistic regression models were developed for International Society of Urological Pathology (ISUP) Grade Group (GG) ≥ 2 and GG ≥ 3 using noninvasive prebiopsy parameters: age, (log-)prostate-specific antigen (PSA) density, PI-RADS 5 lesion presence, extraprostatic extension (EPE) on MRI, and SUVmax of the prostate. Models with and without SUVmax were compared using Likelihood ratio tests and area under the curve (AUC). DeLong's test was used to compare the AUCs.

RESULTS

The study included 386 patients, with 262 (68%) having ISUP GG ≥ 2 and 180 (47%) having ISUP GG ≥ 3. Including SUVmax significantly improved both models' goodness of fit (p < 0.001). The GG ≥ 2 model had a higher AUC with SUVmax 89.16% (95% confidence interval [CI]: 86.06%-92.26%) than without 87.34% (95% CI: 83.93%-90.76%) (p = 0.026). Similarly, the GG ≥ 3 model had a higher AUC with SUVmax 82.51% (95% CI: 78.41%-86.6%) than without 79.33% (95% CI: 74.84%-83.83%) (p = 0.003). The SUVmax inclusion improved the GG ≥ 3 model's calibration at higher probabilities.

CONCLUSION

SUVmax of the prostate on PSMA PET/CT potentially improves diagnostic accuracy in predicting the likelihood of csPCa in prostate biopsy.

Navigating the complexity of PI3K/AKT pathway in HER-2 negative breast cancer: biomarkers and beyond

The results of the SOLAR-1 and CAPItello-291, highlight the benefit of the ɑ-selective phosphoinositide 3-Kinase Pathway inhibitor (PI3Ki) alpelisib and the AKT inhibitor (AKTi) capivasertib in patients with hormone receptor-positive (HR+)/Human Epidermal Growth Factor Receptor 2 (HER2)- negative metastatic breast cancer (mBC) that have PIK3CA/AKT1/PTEN tumour alterations. Although effective, these drugs are associated with significant toxicities, which often limit their use, particularly in frail patients. Following the recent incorporation of these agents into clinical practice, and with many others currently in development, significant challenges have emerged, particularly those regarding biomarkers for patient selection. This review will discuss biomarkers of response and their resistance to PI3K/AKT inhibitors (PI3K/AKTis) in HR+/HER- BC in early and advanced settings to ascertain which populations will most benefit from these drugs. Of the biomarkers that were analysed, such as PIK3CA, AKT, PTEN mutations, insulin levels, 18 F-FDG-PET/TC, only the PIK3CA-mutations (PIK3CA-mut) and the AKT pathway alterations seem to have a predictive value for treatments with alpelisib and capivasertib. However, due to the retrospective and exploratory nature of the study, the data did not provide conclusive results. In addition, the different methods used to detect PIK3CA/AKT1/PTEN alterations underline the fact that the optimal diagnostic companion has yet to be established. We have summarised the clinical data on the approved and discontinued agents targeting this pathway and have assessed the drugs development, successes, and failures. Finally, because of tumour heterogeneity, we emphasise the importance of reassessing the mutational status of PI3KCA in both metastatic tissue and blood at the time of disease progression to better tailor treatment for patients.

Noninvasive Assessment of Diabetic Kidney Disease With MRI: Hype or Hope?

Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T1- or T2-weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision-making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break-through are discussed including biological validation of the MRI findings, large-scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Artificial intelligence in neurology: opportunities, challenges, and policy implications

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy

Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.

Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics

OBJECTIVES

This study aims to develop and validate a virtual biopsy model to predict microsatellite instability (MSI) status in preoperative gastric cancer (GC) patients based on clinical information and the radiomics of deep learning algorithms.

METHODS

A total of 223 GC patients with MSI status detected by postoperative immunohistochemical staining (IHC) were retrospectively recruited and randomly assigned to the training (n = 167) and testing (n = 56) sets in a 3:1 ratio. In the training set, 982 high-throughput radiomic features were extracted from preoperative abdominal dynamic contrast-enhanced CT (CECT) and screened. According to the deep learning multilayer perceptron (MLP), 15 optimal features were optimized to establish the radiomic feature score (Rad-score), and LASSO regression was used to screen out clinically independent predictors. Based on logistic regression, the Rad-score and clinically independent predictors were integrated to build the clinical radiomics model and visualized as a nomogram and independently verified in the testing set. The performance and clinical applicability of hybrid model in identifying MSI status were evaluated by the area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve (DCA).

RESULTS

The AUCs of the clinical image model in training set and testing set were 0.883 [95% CI: 0.822-0.945] and 0.802 [95% CI: 0.666-0.937], respectively. This hybrid model showed good consistency in the calibration curve and clinical applicability in the DCA curve, respectively.

CONCLUSIONS

Using preoperative imaging and clinical information, we developed a deep-learning-based radiomics model for the non-invasive evaluation of MSI in GC patients. This model maybe can potentially support clinical treatment decision making for GC patients.

Clinically Significant Prostate Cancer Diagnosis Without Histological Proof: A Possibility in the Prostate-specific Membrane Antigen Era?

Magnetic resonance imaging (MRI) has resulted in a reduction in the number of patients indicated for prostate biopsy. Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has recently shown additional value in detecting clinically significant prostate cancer (csPCa). Combining these imaging modalities allows such specific prediction of the presence of csPCa that the need for histological confirmation may be obsolete. We retrospectively analyzed PSMA PET/CT scans performed in the primary staging of PCa in the past 2 yr in our center (n = 451). All 74 patients with a PSMA ligand maximum standardized uptake value (SUVmax) of ≥16 had csPCa (grade group ≥2). Of the 185 patients with a combination of a Prostate Imaging-Reporting and Data System score ≥4 and SUVmax ≥8, 98% had csPCa. A nomogram combining predictive factors should be developed to identify patients in whom biopsy could theoretically be avoided. Nevertheless, biopsy will remain indispensable in patients with indefinite risk of csPCa and can provide important additional information.

Patient summary

Using patient data from our center, we found that addition of a special type of scan based on prostate-specific membrane antigen could help in the diagnosis of clinically significant prostate cancer without the need for prostate biopsy. Direct therapy without biopsy confirmation of cancer might be possible for a highly select group of patients.

Comparative Analysis for the Distinction of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma in Computed Tomography Imaging Using Machine Learning Radiomics Analysis

Background: ChRCC and RO are two types of rarely occurring renal tumors that are difficult to distinguish from one another based on morphological features alone. They differ in prognosis, with ChRCC capable of progressing and metastasizing, but RO is benign. This means discrimination of the two tumors is of crucial importance. Objectives: The purpose of this research was to develop and comprehensively evaluate predictive models that can discriminate between ChRCC and RO tumors using Computed Tomography (CT) scans and ML-Radiomics texture analysis methods. Methods: Data were obtained from 78 pathologically confirmed renal masses, scanned at two institutions. Data from the two institutions were combined to form a third set resulting in three data cohorts, i.e., cohort 1, 2 and combined. Contrast-enhanced scans were used and the axial cross-sectional slices of each tumor were extracted from the 3D data using a semi-automatic segmentation technique for both 2D and 3D scans. Radiomics features were extracted before and after applying filters and the dimensions of the radiomic features reduced using the least absolute shrinkage and selection operator (LASSO) method. Synthetic minority oversampling technique (SMOTE) was applied to avoid class imbalance. Five ML algorithms were used to train models for predictive classification and evaluated using 5-fold cross-validation. Results: The number of selected features with good model performance was 20, 40 and 6 for cohorts 1, 2 and combined, respectively. The best model performance in cohorts 1, 2 and combined had an excellent Area Under the Curve (AUC) of 1.00 ± 0.000, 1.00 ± 0.000 and 0.87 ± 0.073, respectively. Conclusions: ML-based radiomics signatures are potentially useful for distinguishing ChRCC and RO tumors, with a reliable level of performance for both 2D and 3D scanning.