T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

MRI-based virtual pathology of the prostate

Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.

An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

Objectives

To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE).

Methods

Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC).

Results

Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2).

Conclusions

Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).

Magnetic Resonance Fingerprinting: A Review of Clinical Applications

ABSTRACT

Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.

Imaging in Interventional Radiology: 2043 and Beyond

Since its inception in the early 20th century, interventional radiology (IR) has evolved tremendously and is now a distinct clinical discipline with its own training pathway. The arsenal of modalities at work in IR includes x-ray radiography and fluoroscopy, CT, MRI, US, and molecular and multimodality imaging within hybrid interventional environments. This article briefly reviews the major developments in imaging technology in IR over the past century, summarizes technologies now representative of the standard of care, and reflects on emerging advances in imaging technology that could shape the field in the century ahead. The role of emergent imaging technologies in enabling high-precision interventions is also briefly reviewed, including image-guided ablative therapies.

Comparing Magnetic Resonance Fingerprinting (MRF) and the MAGiC Sequence for Simultaneous T1 and T2 Quantitative Measurements in the Female Pelvis: A Prospective Study

The aim of this study was to explore the potential of magnetic resonance fingerprinting (MRF), an emerging quantitative MRI technique, in measuring relaxation values of female pelvic tissues compared to the conventional magnetic resonance image compilation (MAGiC) sequence. The study included 32 female patients who underwent routine pelvic MRI exams using anterior and posterior array coils on a 3T clinical scanner. Our findings demonstrated significant correlations between MRF and MAGiC measured T1 and T2 values (p < 0.0001) for various pelvic tissues, including ilium, femoral head, gluteus, obturator, iliopsoas, erector spinae, uterus, cervix, and cutaneous fat. The tissue contrasts generated from conventional MRI and synthetic MRF also showed agreement in bone, muscle, and uterus for both T1-weighted and T2-weighted images. This study highlights the strengths of MRF in providing simultaneous T1 and T2 mapping. MRF offers distinct tissue contrast and has the potential for accurate diagnosis of female pelvic diseases, including tumors, fibroids, endometriosis, and pelvic inflammatory disease. Additionally, MRF shows promise in monitoring disease progression or treatment response. Overall, the study demonstrates the potential of MRF in the field of female pelvic organ imaging and suggests that it could be a valuable addition to the clinical practice of pelvic MRI exams. Further research is needed to establish the clinical utility of MRF and to develop standardized protocols for its implementation in clinical practice.

Glucocorticoid nanoformulations relieve chronic pelvic pain syndrome and may alleviate depression in mice

BACKGROUND

Chronic pelvic pain syndrome (CPPS) is a typical symptom of chronic prostatitis (CP) in males that may cause abnormal urination, sexual dysfunction, or depression and significantly affect the quality of life of the patient. Currently, there is no effective treatment for CPPS due to its recurrence and intractability. For synergistic CPPS therapy, we developed pH/reactive oxygen species (ROS) dual-responsive dexamethasone (Dex) nanoformulations using a ROS-responsive moiety and phytochemical modified α-cyclodextrin (α-CD) as the carrier.

RESULTS

Dex release from the nanoformulations can be controlled in acidic and/or ROS-rich microenvironments. The fabricated Dex nanoformulations can also be efficiently internalized by lipopolysaccharide (LPS)-stimulated macrophages, prostatic epithelial cells, and stromal cells. Moreover, the levels of proinflammatory factors (e.g., TNF-α, IL-1β, and IL-17 A) in these cells were significantly decreased by Dex nanoformulations treatment through the release of Dex, phytochemical and elimination of ROS. In vivo experiments demonstrated notable accumulation of the Dex nanoformulations in prostate tissue to alleviate the symptoms of CPPS through the downregulation of proinflammatory factors. Interestingly, depression in mice may be relieved due to alleviation of their pelvic pain.

CONCLUSION

We fabricated Dex nanoformulations for the effective management of CPPS and alleviation of depression in mice.

Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer

Background

around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─).

Objective

To quantify the differences between MR visible (MRI+) and MRI^─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI).

Methods

This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI^─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (D_v) comprised 52 patients from a single institution, the remaining 112 patients were used for training (D_t). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on D_t to identify radiomic features associated with MRI^─ and MRI⁺ CsPCa to generate corresponding risk scores R^MRI─ and R^MRI+. R_bpMRI was further generated by integrating R^MRI─ and R^MRI+. Statistical significance was determined using the Wilcoxon signed-rank test.

Results

Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI^─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI^─ and MRI⁺ CsPCa (p < 0.05). R_bpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of R^MRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on D_v. R_bpMRI correctly reclassified 10 out of 14 MRI^─ CsPCa on D_v.

Conclusion

Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI^─ CsPCa. These features could assist in CsPCa identification on bpMRI.

Magnetic resonance fingerprinting based comprehensive quantification of T1 and T2 values of the background prostatic peripheral zone: Correlation with clinical and demographic features

PURPOSE

To quantify and assess the distribution of MR fingerprinting (MRF)-derived T1 and T2 values of the whole prostatic peripheral zone (PZ), and perform subgroup analyses according to clinical and demographic features.

METHOD

One hundred and twenty-four patients with prostate MR exams and MRF-based T1 and T2 maps of the prostatic apex, mid gland, and base were identified from our database and included. Regions of interest encompassing the right and left lobes of the PZ were drawn for each axial slice on the T2 map and copied to the T1 map. Clinical data were obtained from medical records. Kruskal-Wallis test was used for assessing differences between subgroups and the Spearman coefficient was used for assessing any correlations.

RESULTS

Mean T1 and T2 values were 1941 and 88 ms, respectively, for the whole-gland, 1884 and 83 ms for the apex, 1974 and 92 ms for the mid-gland, 1966 and 88 ms for the base. T1 values were weakly negatively correlated with PSA values, while T1 and T2 values were weakly positively correlated with prostate weight and moderately positively correlated with PZ width. Finally, patients with PI-RADS 1 scores had higher T1 and T2 values of the whole PZ, compared with those with scores 2-5.

CONCLUSION

Mean T1 and T2 values of the background PZ of the whole gland were 1941 ± 313 and 88 ± 39 ms, respectively. Among clinical and demographic factors, there was a significant positive correlation between T1 and T2 values and PZ width.

Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI

This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction f_ic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R² = 0.79-0.98; CV = 1-7%; ICC = 92-98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3.

ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features

OBJECTIVES

To quantify intra-tumor heterogeneity (ITH) in non-small cell lung cancer (NSCLC) from computed tomography (CT) images.

METHODS

We developed a quantitative ITH measurement-ITHscore-by integrating local radiomic features and global pixel distribution patterns. The associations of ITHscore with tumor phenotypes, genotypes, and patient's prognosis were examined on six patient cohorts (n = 1399) to validate its effectiveness in characterizing ITH.

RESULTS

For stage I NSCLC, ITHscore was consistent with tumor progression from stage IA1 to IA3 (p < 0.001) and captured key pathological change in terms of malignancy (p < 0.001). ITHscore distinguished the presence of lymphovascular invasion (p = 0.003) and pleural invasion (p = 0.001) in tumors. ITHscore also separated patient groups with different overall survival (p = 0.004) and disease-free survival conditions (p = 0.005). Radiogenomic analysis showed that the level of ITHscore in stage I and stage II NSCLC is correlated with heterogeneity-related pathways. In addition, ITHscore was proved to be a stable measurement and can be applied to ITH quantification in head-and-neck cancer (HNC).

CONCLUSIONS

ITH in NSCLC can be quantified from CT images by ITHscore, which is an indicator for tumor phenotypes and patient's prognosis.

KEY POINTS

• ITHscore provides a radiomic quantification of intra-tumor heterogeneity in NSCLC. • ITHscore is an indicator for tumor phenotypes and patient's prognosis. • ITHscore has the potential to be generalized to other cancer types such as HNC.

Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study

OBJECTIVES

To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms.

METHODS

3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region.

RESULTS

Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05).

CONCLUSION

Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival.

KEY POINTS

• 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.

Biparametric MR signal characteristics can predict histopathological measures of prostate cancer

OBJECTIVES

The aim of this study was to establish a new data-driven metric from MRI signal intensity that can quantify histopathological characteristics of prostate cancer.

METHODS

This retrospective study was conducted on 488 patients who underwent biparametric MRI (bp-MRI), including T2-weighted imaging (T2W) and apparent diffusion coefficient (ADC) of diffusion-weighted imaging, and having biopsy-proven prostate cancer between August 2011 and July 2015. Forty-two of the patients who underwent radical prostatectomy and the rest of 446 patients constitute the labeled and unlabeled datasets, respectively. A deep learning model was built to predict the density of epithelium, epithelial nuclei, stroma, and lumen from bp-MRI, called MR-driven tissue density. On both the labeled validation set and the whole unlabeled dataset, the quality of MR-driven tissue density and its relation to bp-MRI signal intensity were examined with respect to different histopathologic and radiologic conditions using different statistical analyses.

RESULTS

MR-driven tissue density and bp-MRI of 446 patients were evaluated. MR-driven tissue density was significantly related to bp-MRI (p < 0.05). The relationship was generally stronger in cancer regions than in benign regions. Regarding cancer grades, significant differences were found in the intensity of bp-MRI and MR-driven tissue density of epithelium, epithelial nuclei, and stroma (p < 0.05). Comparing MR true-negative to MR false-positive regions, MR-driven lumen density was significantly different, similar to the intensity of bp-MRI (p < 0.001).

CONCLUSIONS

MR-driven tissue density could serve as a reliable histopathological measure of the prostate on bp-MRI, leading to an improved understanding of prostate cancer and cancer progression.

KEY POINTS

• Semi-supervised deep learning enables non-invasive and quantitative histopathology in the prostate from biparametric MRI. • Tissue density derived from biparametric MRI demonstrates similar characteristics to the direct estimation of tissue density from histopathology images. • The analysis of MR-driven tissue density reveals significantly different tissue compositions among different cancer grades as well as between MR-positive and MR-negative benign.

Comparison of single-shot EPI and multi-shot EPI in prostate DWI at 3.0 T

In prostate MRI, single-shot EPI (ssEPI) DWI still suffers from distortion and blurring. Multi-shot EPI (msEPI) overcomes the drawbacks of ssEPI DWI. The aim of this article was to compare the image quality and diagnostic performance for clinically significant prostate cancer (csPC) between ssEPI DWI and msEPI DWI. This retrospective study included 134 patients with suspected PC who underwent 3.0 T MRI and subsequent MRI-guided biopsy. Three radiologists independently assessed anatomical distortion, prostate edge clarity, and lesion conspicuity score for pathologically confirmed csPC. Lesion apparent diffusion coefficient (ADC) and benign ADC were also calculated. In 17 PC patients who underwent prostatectomy, three radiologists independently assessed eight prostate regions by DWI score in PI-RADS v 2.1. Anatomical distortion and prostate edge clarity were significantly higher in msEPI DWI than in ssEPI DWI in the three readers. Lesion conspicuity score was significantly higher in msEPI DWI than in ssEPI DWI in reader 1 and reader 3. Regarding discrimination ability between PC with GS ≤ 3 + 4 and PC with GS ≥ 4 + 3 using lesion ADC, AUC was comparable between ssEPI DWI and msEPI DWI. For diagnostic performance of csPC using DWI score, AUC was comparable between msEPI DWI and ssEPI DWI in all readers. Compared with ssEPI DWI, msEPI DWI had improved image quality and similar or higher diagnostic performance.

MR fingerprinting of the prostate

Multiparametric magnetic resonance imaging (mpMRI) has been adopted as the key tool for detection, localization, characterization, and risk stratification of patients suspected to have prostate cancer. Despite advantages over systematic biopsy, the interpretation of prostate mpMRI has limitations including a steep learning curve, leading to considerable interobserver variation. There is growing interest in clinical translation of quantitative imaging techniques for more objective lesion assessment. However, traditional mapping techniques are slow, precluding their use in the clinic. Magnetic resonance fingerprinting (MRF) is an efficient approach for quantitative maps of multiple tissue properties simultaneously. The T₁ and T₂ values obtained with MRF have been validated with phantom studies as well as in normal volunteers and patients. Studies have shown that MRF-derived T₁ and T₂ along with ADC values are all significant independent predictors in the differentiation between normal prostate tissue and prostate cancer, and hold promise in differentiating low and intermediate/high-grade cancers. This review seeks to introduce the basics of the prostate MRF technique, discuss the potential applications of prostate MRF for the characterization of prostate cancer, and describes ongoing areas of research.

Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review

This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000–2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility.

Infiltrative growth pattern of prostate cancer is associated with lower uptake on PSMA PET and reduced diffusion restriction on mpMRI

Purpose

Recently, a significant association was shown between novel growth patterns on histopathology of prostate cancer (PCa) and prostate-specific membrane antigen (PSMA) uptake on [⁶⁸Ga]PSMA-PET. It is the aim of this study to evaluate the association between these growth patterns and ADC (mm²/1000 s) values in comparison to [⁶⁸Ga]PSMA uptake on PET/MRI.

Methods

We retrospectively evaluated patients who underwent [⁶⁸Ga]PSMA PET/MRI for staging or biopsy guidance, followed by radical prostatectomy at our institution between 07/2016 and 01/2020. The dominant lesion per patient was selected based on histopathology and correlated to PET/MRI in a multidisciplinary meeting, and quantified using SUV_max for PSMA uptake and ADC_mean for diffusion restriction. PCa growth pattern was classified as expansive (EXP) or infiltrative (INF) according to its properties of forming a tumoral mass or infiltrating diffusely between benign glands by two independent pathologists. Furthermore, the corresponding WHO2016 ISUP tumor grade was evaluated. The t test was used to compare means, Pearson’s test for categorical correlation, Cohen’s kappa test for interrater agreement, and ROC curve to determine the best cutoff.

Results

Sixty-two patients were included (mean PSA 11.7 ± 12.5). The interrater agreement between both pathologists was almost perfect with κ = 0.81. While 25 lesions had an EXP-growth with an ADC_mean of 0.777 ± 0.109, 37 showed an INF-growth with a significantly higher ADC_mean of 1.079 ± 0.262 (p < 0.001). We also observed a significant difference regarding PSMA SUV_max for the EXP-growth (19.2 ± 10.9) versus the INF-growth (9.4 ± 6.2, p < 0.001). Within the lesions encompassing the EXP- or the INF-growth, no significant correlation between the ISUP groups and ADC_mean could be observed (p = 0.982 and p = 0.861, respectively).

Conclusion

PCa with INF-growth showed significantly lower SUV_max and higher ADC_mean values compared to PCa with EXP-growth. Within the growth groups, ADC_mean values were independent from ISUP grading.

Supplementary information

The online version contains supplementary material available at 10.1007/s00259-022-05787-9.

Magnetic resonance fingerprinting in prostate cancer before and after contrast enhancement

OBJECTIVES

To assess the apparent diffusion coefficient (ADC) values and the T1 and T2 values derived from nonenhanced (NE) and contrast-enhanced (CE) magnetic resonance fingerprinting (MRF) in the prostate gland and to evaluate differences in values among prostate cancer, the normal peripheral zone (PZ) and the normal transition zone (TZ).

METHODS

Fifty-seven patients (median age, 73 years; range, 48-86) with prostate cancer who underwent multiparametric MRI including NE and CE MRF were included in this study. T1 and T2 values were extracted from NE and CE MRF, respectively. Five quantitative values (the ADC, NE T1, NE T2, CE T1 and CE T2 values) were measured in three areas: prostate cancer, PZ and TZ. We compared the values among the three areas and evaluated the differences between NE MRF and CE MRF values.

RESULTS

ADC values and MRF-derived values were significantly higher in PZ than prostate cancer or TZ (p < 0.001). TZ had a significantly lower CE T1 but significantly higher values of the other variables than prostate cancer (p < 0.001). The T1 values in all three areas and the T2 values in prostate cancer and TZ were significantly lower on CE MRF than on NE MRF (p < 0.001).

CONCLUSIONS

Quantitative analysis of NE and CE MRI can be conducted by using the MRF technique. The ADC value and the T1 and T2 values from CE MRF and NE MRF were found to be significantly different between prostate cancer and normal prostate tissue.

ADVANCES IN KNOWLEDGE

The T1 and T2 values from contrast-enhanced MR fingerprinting are significantly different between prostate cancer and normal prostate tissue.

Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

In the last decade, the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering "sub-visual" prognostic image cues from the histopathological image. While we are getting more knowledge and experience in digital pathology, the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay. In this paper, we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis. It includes: correlation of pathomics and genomics; fusion of pathomics and genomics; fusion of pathomics and radiomics. We also present challenges, potential opportunities, and avenues for future work.

3D MR fingerprinting (MRF) for simultaneous T1 and T2 quantification of the bone metastasis: Initial validation in prostate cancer patients

PURPOSE

To investigate the feasibility of using 3-dimensional MRF for bone marrow evaluation in the field of view of prostate MRI for T1 and T2 quantification of prostate cancer bone metastases, as well as comparing it to the ADC value.

METHODS

In this retrospective study, 30 prostate MRIs were included: 14 cases with prostate cancer bone metastasis and 16 cases without prostate cancer (control). MRF was obtained twice before (nonenhanced [NE] MRF) and after contrast injection (contrast-enhanced [CE] MRF), and T1 and T2 maps were generated from each MRF. Two radiologists independently drew regions of interest (ROIs) on the MRF maps and the ADC maps. Mann-Whitney U tests and the area under the receiver operating characteristic curve (AUROC) evaluated the two-reader means of T1, T2 and ADC values between bone metastasis and normal bone.

RESULTS

There were 83 ROIs, including 39 bone metastases and 44 normal bone. The two-reader average ADC, NE T2 and CE T2 values were significantly lower and NE T1 and CE T1 values were significantly higher in metastatic bone compared with normal bone (P < 0.001). The AUROC of the ADC was lowest (0.685), which was significantly lower than those of NE T1 (1.0, P = 0.001), NE T2 (0.932, P = 0.004), and CE T2 (0.876, P = 0.031).

CONCLUSION

MRF to assess the pelvic bone during a prostate gland evaluation provides a reliable parametric map for skeletal work-up. With higher diagnostic performance than the ADC value, NE MRF is a potential alternative for quantifying bone marrow metastases in prostate cancer patients.

Repeatability of MR fingerprinting in normal cervix and utility in cervical carcinoma

Background

Magnetic resonance fingerprinting (MRF) is a fast-imaging acquisition technique that generates quantitative and co-registered parametric maps. The aim of this feasibility study was to evaluate the agreement between MRF and phantom reference values, scan-rescan repeatability of MRF in normal cervix, and its ability to distinguish cervical carcinoma (CC) from normal cervical tissues.

Methods

An International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) phantom was scanned using MRF 15 times over 65 days. Agreement between MRF and phantom reference T1 and T2 values was assessed by linear regression. Healthy volunteers and patients with suspected CC were prospectively recruited. MRF was repeated twice for healthy volunteers (MRF1 and MRF2). Volumes of interest of normal cervical tissues and CC were delineated on T1 and T2 maps. MRF scan-rescan repeatability was evaluated by Bland-Altman plots, within-subject coefficients of variation (wCV), and intraclass correlation coefficients (ICC). T1 and T2 values were compared between CC and normal cervical tissues using Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic efficiency.

Results

Strong correlations were observed between MRF and phantom (R²=0.999 for T1, 0.981 for T2). Twelve healthy volunteers (28.7±5.1 years) and 28 patients with CC (54.6±15.2 years) were recruited for the in-vivo experiments. Repeatability of MRF parameters were wCV <3% for T1, <5% for T2 and ICC ≥0.92 for T1, ≥0.94 for T2. T1 value of CC (1,529±112 ms) was higher than normal mucosa [MRF1: 1,430±129 ms, MRF2: 1,440±130 ms; P=0.031, area under the curve (AUC) ≥0.717] and normal stroma (MRF1: 1,258±101 ms, MRF2: 1,276±105 ms; P<0.001, AUC ≥0.946). T2 value of CC (69±9 ms) was lower than normal mucosa (MRF1: 88±16 ms, MRF2: 87±13 ms; P<0.001, AUC ≥0.854), but was not different from normal stroma (P=0.919).

Conclusions

Excellent agreement was observed between MRF and phantom reference values. MRF exhibited excellent scan-rescan repeatability in normal cervix with potential value in differentiating CC from normal cervical tissues.

Magnetic resonance fingerprinting: an overview

Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.

Feasibility of Novel Three-Dimensional Magnetic Resonance Fingerprinting of the Prostate Gland: Phantom and Clinical Studies

Objective

To evaluate the feasibility of a new three-dimensional (3D) MR fingerprinting (MRF) technique for the prostate gland by conducting phantom and clinical studies.

Materials and Methods

The new 3D MRF technique used in this study enables quick data acquisition and has a high resolution. For the phantom study, the MRF T1 and T2 values in an in-house phantom were compared with those of gold-standard mapping methods using linear regression analysis. For the clinical study, we evaluated 90 patients who underwent prostate imaging with MRF for suspected prostate cancer between September 2019 and February 2020. The mean T1 and T2 values were compared in the peripheral zone, transition zone, and focal lesions using paired t tests. The differences in the T1 and T2 values according to cancer aggressiveness were evaluated using one-way analysis of variance.

Results

In the phantom study, the MRF T1 and T2 values showed a perfect correlation with the gold-standard T1 and T2 values (R > 0.99). In the clinical study, the T1 and T2 values in the peripheral zone were significantly higher than those in the transitional zone (p < 0.001, both). The T1 and T2 values in prostate cancer were significantly lower than those in the peripheral and transitional zones. The higher the grade of cancer, the lower the T2 values.

Conclusion

The T1 and T2 values obtained from the 3D MRF showed a perfect correlation with the gold standard values in the phantom study. Differences in the T1 and T2 values among the different zones of the prostate gland were identified using 3D MRF in patients.

Magnetic resonance fingerprinting for preoperative differentiation between gonadotroph and non-gonadotroph pituitary macroadenomas

OBJECTIVES

To use magnetic resonance fingerprinting (MRF)-derived T1 and T2 values to differentiate gonadotroph from non-gonadotroph pituitary macroadenomas based on the 2017 World Health Organization classification of pituitary adenomas.

METHODS

A total of 57 patients with suspected pituitary macroadenomas were enrolled for analyses in this study between May 2018 and January 2020. Conventional magnetic resonance imaging (MRI) and MRF were performed in all patients before surgery using a 3-T MRI scanner. MRF-derived T1 and T2 values were compared between the gonadotroph and non-gonadotroph pituitary macroadenomas using a Mann-Whitney U test. The Knosp classification was used to evaluate cavernous sinus invasion by the adenomas. Receiver operating characteristic analyses were used to determine the diagnostic performance of T1 and T2 values.

RESULTS

Quantitative T1 and T2 values yielded from MRF of gonadotroph pituitary macroadenomas were significantly higher than those of the non-gonadotroph pituitary macroadenomas (p < 0.001 and = 0.002, respectively). The AUC for the T2 value (0.888) was significantly greater than that for the T1 value (0.742) (p = 0.034). The AUC for combined T1 and T2 values was 0.885. Non-gonadotroph pituitary macroadenomas were more likely to invade the cavernous sinus than gonadotroph pituitary macroadenomas (55% vs 26%, p = 0.026).

CONCLUSIONS

MRF may help to preoperatively differentiate between gonadotroph and non-gonadotroph pituitary macroadenomas and may be useful in guiding the treatment of these adenomas.

KEY POINTS

• Somatostatin receptor type 3 is the most abundant receptor subtype in gonadotroph pituitary adenomas. • Magnetic resonance fingerprinting may help to preoperatively differentiate between gonadotroph and non-gonadotroph pituitary macroadenomas. • Magnetic resonance fingerprinting shows potential for guiding the treatment of pituitary macroadenomas.