Characterization of prostate cancer using diffusion tensor imaging: A new perspective

The contribution of the nervous system in the cancer progression

Cancer progression is driven by genetic mutations, environmental factors, and intricate interactions within the tumor microenvironment (TME). The TME comprises of diverse cell types, such as cancer cells, immune cells, stromal cells, and neuronal cells. These cells mutually influence each other through various factors, including cytokines, vascular perfusion, and matrix stiffness. In the initial or developmental stage of cancer, neurotrophic factors such as nerve growth factor, brain-derived neurotrophic factor, and glial cell line-derived neurotrophic factor are associated with poor prognosis of various cancers by communicating with cancer cells, immune cells, and peripheral nerves within the TME. Over the past decade, research has been conducted to prevent cancer growth by controlling the activation of neurotrophic factors within tumors, exhibiting a novel attemt in cancer treatment with promising results. More recently, research focusing on controlling cancer growth through regulation of the autonomic nervous system, including the sympathetic and parasympathetic nervous systems, has gained significant attention. Sympathetic signaling predominantly promotes tumor progression, while the role of parasympathetic signaling varies among different cancer types. Neurotransmitters released from these signalings can directly or indirectly affect tumor cells or immune cells within the TME. Additionally, sensory nerve significantly promotes cancer progression. In the advanced stage of cancer, cancer-associated cachexia occurs, characterized by tissue wasting and reduced quality of life. This process involves the pathways via brainstem growth and differentiation factor 15-glial cell line-derived neurotrophic factor receptor alpha-like signaling and hypothalamic proopiomelanocortin neurons. Our review highlights the critical role of neurotrophic factors as well as central nervous system on the progression of cancer, offering promising avenues for targeted therapeutic strategies. [BMB Reports 2024; 57(4): 167-175].

Acquisition Parameters Influence Diffusion Metrics Effectiveness in Probing Prostate Tumor and Age-Related Microstructure

This study aimed to investigate the Diffusion-Tensor-Imaging (DTI) potential in the detection of microstructural changes in prostate cancer (PCa) in relation to the diffusion weight (b-value) and the associated diffusion length l_D. Thirty-two patients (age range = 50-87 years) with biopsy-proven PCa underwent Diffusion-Weighted-Imaging (DWI) at 3T, using single non-zero b-value or groups of b-values up to b = 2500 s/mm². The DTI maps (mean-diffusivity, MD; fractional-anisotropy, FA; axial and radial diffusivity, D// and D┴), visual quality, and the association between DTI-metrics and Gleason Score (GS) and DTI-metrics and age were discussed in relation to diffusion compartments probed by water molecules at different b-values. DTI-metrics differentiated benign from PCa tissue (p ≤ 0.0005), with the best discriminative power versus GS at b-values ≥ 1500 s/mm², and for b-values range 0-2000 s/mm², when the l_D is comparable to the size of the epithelial compartment. The strongest linear correlations between MD, D//, D┴, and GS were found at b = 2000 s/mm² and for the range 0-2000 s/mm². A positive correlation between DTI parameters and age was found in benign tissue. In conclusion, the use of the b-value range 0-2000 s/mm² and b-value = 2000 s/mm² improves the contrast and discriminative power of DTI with respect to PCa. The sensitivity of DTI parameters to age-related microstructural changes is worth consideration.

Diagnostic utility of three Tesla diffusion tensor imaging in prostate cancer: correlation with Gleason score values

Background

Preoperative assessment of prostate cancer (PCa) aggressiveness is a prerequisite to provide specific management options. The Gleason score (GS) obtained from prostatic biopsy or surgery is crucial for the evaluation of PCa aggressiveness and personalized treatment planning. Diffusion tensor imaging (DTI) provides valuable information about microstructural properties of prostatic tissue. The most common prostate DTI measures are the fractional anisotropy (FA) and median diffusivity (MD) can give more information regarding the biophysical characteristics of prostate tissue. We aimed to explore the correlation of these DTI parameters with GS levels in PCa patients that can affect the management protocol of PCa.

Results

The computed area under curve (AUC) of the FA values used to differentiate cancer patients from control group was (0.90) with cutoff point to differentiate both groups were ≥ 0.245. The computed sensitivity, specificity, positive and negative predictive values were (84%, 80%, 95.5%, and 50%), respectively, with accuracy 83.3%. FA showed high positive correlation with Gleason score (p value < 0.001). Median diffusivity (MD) showed negative correlation with GS with statistically significant results (p value  = 0.013). PCa fiber bundles were dense, orderly arranged, without interruption in the low grade, and slightly disorganized in the intermediate group. However, in the high-grade group, the fiber bundles were interrupted, irregularly arranged, and absent at the site of cancerous foci.

Conclusions

Combined quantitative parameter values (FA and MD values) and parametric diagrams (FA and DTI maps) can be utilized to evaluate prostate cancer aggressiveness and prognosis, helping in the improvement of the management protocol of PCa patients.

Diffusion Tensor Imaging Technology to Quantitatively Assess Abnormal Changes in Patients With Thyroid-Associated Ophthalmopathy

Objective

We aim to investigate the feasibility of using diffusion tensor imaging (DTI) to evaluate changes in extraocular muscles (EOMs) and lacrimal gland (LG) in patients with thyroid-associated ophthalmopathy (TAO) and to evaluate disease severity.

Materials and Methods

A total of 74 participants, including 17 healthy controls (HCs), 22 patients with mild TAO, and 35 patients with moderate-severe TAO, underwent 3-Tesla DTI to measure fractional anisotropy (FA) and mean diffusivity (MD) of the EOMs and LG. Ophthalmological examinations, including visual acuity, exophthalmos, intraocular pressure, and fundoscopy, were performed. FA and MD values were compared among patients with different disease severity. Multiple linear regression was adopted to predict the impact of clinical variables on DTI parameters of orbital soft tissue.

Results

TAO patients’ EOMs and LG showed significantly lower FA values and higher MD compared to HCs’ (P < 0.05). Moderate-severe TAO patients’ EOMs and LG had dramatically lower FA and higher MD compared with HCs (P < 0.05). In addition, only the DTI parameters of the medial rectus were considerably different between mild and moderate-severe TAO patients (P = 0.017, P = 0.021). Multiple linear regression showed that disease severity had a significant impact on the DTI parameters of orbital soft tissue.

Conclusion

DTI is a useful tool for detecting microstructural changes in TAO patients’ orbital soft tissue. DTI findings, especially medial rectus DTI parameters, can help to indicate the disease severity in TAO patients.

The nervous system: Orchestra conductor in cancer, regeneration, inflammation and immunity

Although the role of nerves in stimulating cellular growth and dissemination has long been described in tissue regeneration studies, until recently a similar trophic role of nerves in disease was not well recognized. However, recent studies in oncology have demonstrated that the growth and dissemination of cancers also requires the infiltration of nerves in the tumor microenvironment. Nerves generate various neurosignaling pathways, which orchestrate cancer initiation, progression, and metastases. Similarly, nerves are increasingly implicated for their regulatory functions in immunity and inflammation. This orchestrator role of nerves in cellular and molecular interactions during regeneration, cancer, immunity, and inflammation offers new possibilities for targeting or enhancing neurosignaling in human health and diseases.

Quantitative diffusion MRI of the abdomen and pelvis

Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.

Usefulness of readout-segmented EPI-based diffusion tensor imaging of lacrimal gland for detection and disease staging in thyroid-associated ophthalmopathy

Background

Dysfunction of lacrimal gland (LG) gains increasing attention in patients with thyroid-associated ophthalmopathy (TAO), while the underlying pathological change is still not fully established. This study aimed to evaluate the utility of readout-segmented echo-planar imaging (rs-EPI)-based diffusion tensor imaging (DTI) in non-invasively detecting microstructural alterations of LG in patients with TAO, as well as in discriminating disease activity.

Methods

Thirty TAO patients and 15 age- and sex- matched healthy controls, who underwent rs-EPI-based DTI, were retrospectively enrolled. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) of LG, and clinical-endocrinological variables were collected and compared. The correlations between FA and ADC values of LG and serum thyroid biochemical markers were also assessed.

Results

TAO group showed significantly lower FA (P < 0.001) and higher ADC (P = 0.014) of LG than healthy group. Active subgroup had significantly lower FA (P < 0.001) and higher ADC (P < 0.001) than inactive subgroup. In TAO group, FA of LG was significantly and negatively correlated with TRAb (r=-0.475, P = 0.008), while ADC of LG showed no significant correlation (P > 0.05). The area under receiver operating characteristic curve of FA was significantly greater than that under curve of ADC for discriminating disease activity (0.832 vs. 0.570, P = 0.009).

Conclusions

rs-EPI-based DTI is a useful tool to characterize the microstructural change of LG in patients with TAO. The derived metrics, particularly FA, can help to reveal disease activity.

Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review

BACKGROUND

Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance.

OBJECTIVE

This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice.

RESULTS

In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications.

CONCLUSIONS

The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.

Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ₁) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

Multiparametric MRI in the Diagnosis of Prostate Cancer: Physical Foundations, Limitations, and Prospective Advances of Diffusion-Weighted MRI

BACKGROUND

 Diffusion-weighted imaging (DWI) is an essential component of the multiparametric MRI exam for the diagnosis and assessment of prostate cancer (PCa). Over the last two decades, various models have been developed to quantitatively correlate the DWI signal with microstructural characteristics of prostate tissue. The simplest approach (ADC: apparent diffusion coefficient) - currently established as the clinical standard - describes monoexponential decay of the DWI signal. While numerous studies have shown an inverse correlation of ADC values with the Gleason score, the ADC model lacks specificity and is based on water diffusion dynamics that are not true in human tissue. This article aims to explain the biophysical limitations of the standard DWI model and to discuss the potential of more complex, advanced DWI models.

METHODS

 This article is a review based on a selective literature review.

RESULTS

 Four phenomenological DWI models are introduced: diffusion tensor imaging, intravoxel incoherent motion, biexponential model, and diffusion kurtosis imaging. Their parameters may potentially improve PCa diagnostics but show varying degrees of statistical significance with respect to the detection and characterization of PCa in current studies. Phenomenological model parameters lack specificity, which has motivated the development of more descriptive tissue models that directly relate microstructural features to the DWI signal. Finally, we present two of such structural models, i. e. the VERDICT (Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors) and RSI (Restriction Spectrum Imaging) model. Both have shown promising results in initial studies regarding the characterization and prognosis of PCa.

CONCLUSION

 Recent developments in DWI techniques promise increasing accuracy and more specific statements about microstructural changes of PCa. However, further studies are necessary to establish a standardized DWI protocol for the diagnosis of PCa.

KEY POINTS

  · DWI is paramount to the mpMRI exam for the diagnosis of PCa.. · Though of clinical value, the ADC model lacks specificity and oversimplifies tissue complexities.. · Advanced phenomenological and structural models have been developed to describe the DWI signal.. · Phenomenological models may improve diagnostics but show inconsistent results regarding PCa assessment.. · Structural models have demonstrated promising results in initial studies regarding PCa characterization..

CITATION FORMAT

· Wichtmann BD, Zöllner FG, Attenberger UI et al. Multiparametric MRI in the Diagnosis of Prostate Cancer: Physical Foundations, Limitations, and Prospective Advances of Diffusion-Weighted MRI. Fortschr Röntgenstr 2021; 193: 399 - 409.

Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Purpose

The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp‐MRI), comprised of T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters.

Materials and methods

In this work, 191 radiomic features were extracted from mp‐MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp‐MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave‐one‐patient‐out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps.

Results

The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features.

Conclusions

Combination of noncontrast mp‐MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.

Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence

Background:

Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer.

Aim:

The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening.

Methods:

This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results.

Results:

CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist.

Conclusion:

When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.

ZBTB46, SPDEF, and ETV6: Novel Potential Biomarkers and Therapeutic Targets in Castration-Resistant Prostate Cancer

Prostate cancer (PCa) is the second most common killer among men in Western countries. Targeting androgen receptor (AR) signaling by androgen deprivation therapy (ADT) is the current therapeutic regime for patients newly diagnosed with metastatic PCa. However, most patients relapse and become resistant to ADT, leading to metastatic castration-resistant PCa (CRPC) and eventually death. Several proposed mechanisms have been proposed for CRPC; however, the exact mechanism through which CRPC develops is still unclear. One possible pathway is that the AR remains active in CRPC cases. Therefore, understanding AR signaling networks as primary PCa changes into metastatic CRPC is key to developing future biomarkers and therapeutic strategies for PCa and CRPC. In the current review, we focused on three novel biomarkers (ZBTB46, SPDEF, and ETV6) that were demonstrated to play critical roles in CRPC progression, epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKI) drug resistance, and the epithelial-to-mesenchymal transition (EMT) for patients treated with ADT or AR inhibition. In addition, we summarize how these potential biomarkers can be used in the clinic for diagnosis and as therapeutic targets of PCa.