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Lorenzo G, Heiselman JS, Liss MA, Miga MI, Gomez H, Yankeelov TE, Reali A, Hughes TJ. A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model. CANCER RESEARCH COMMUNICATIONS 2024; 4:617-633. [PMID: 38426815 PMCID: PMC10906139 DOI: 10.1158/2767-9764.crc-23-0449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/15/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. SIGNIFICANCE Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
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Affiliation(s)
- Guillermo Lorenzo
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| | - Jon S. Heiselman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Michael A. Liss
- Department of Urology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Michael I. Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery, Radiology, and Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Gomez
- School of Mechanical Engineering, Weldon School of Biomedical Engineering, and Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
- Livestrong Cancer Institutes and Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, The University of Texas at Austin, Austin, Texas
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Thomas J.R. Hughes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
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2
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Li C, Wen Y, Xie J, Chen Q, Dang Y, Zhang H, Guo H, Long L. Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study. Front Oncol 2023; 13:1167209. [PMID: 37305565 PMCID: PMC10248416 DOI: 10.3389/fonc.2023.1167209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
Background Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. Purpose To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. Methods 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Results Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). Conclusion DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.
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Affiliation(s)
- Chenhui Li
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yan Wen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jinhuan Xie
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qianjuan Chen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare Ltd., Wuhan, Hubei, China
| | - Hu Guo
- MR Application, Siemens Healthcare Ltd., Changsha, Hunan, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Kuczera S, Langkilde F, Maier SE. Truly reproducible uniform estimation of the ADC with multi-b diffusion data- Application in prostate diffusion imaging. Magn Reson Med 2023; 89:1586-1600. [PMID: 36426737 PMCID: PMC10100221 DOI: 10.1002/mrm.29533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 10/26/2022] [Accepted: 11/01/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE The ADC is a well-established parameter for clinical diagnostic applications, but lacks reproducibility because it is also influenced by the choice diffusion weighting level. A framework is evaluated that is based on multi-b measurement over a wider range of diffusion-weighting levels and higher order tissue diffusion modeling with retrospective, fully reproducible ADC calculation. METHODS Averaging effect from curve fitting for various model functions at 20 linearly spaced b-values was determined by means of simulations and theoretical calculations. Simulation and patient multi-b image data were used to compare the new approach for diffusion-weighted image and ADC map reconstruction with and without Rician bias correction to an active clinical trial protocol probing three non-zero b-values. RESULTS Averaging effect at a certain b-value varies for model function and maximum b-value used. Images and ADC maps from the novel procedure are on-par with the clinical protocol. Higher order modeling and Rician bias correction is feasible, but comes at the cost of longer computation times. CONCLUSIONS Application of the new framework makes higher order modeling more feasible in a clinical setting while still providing patient images and reproducible ADC maps of adequate quality.
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Affiliation(s)
- Stefan Kuczera
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,MedTech West, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Fredrik Langkilde
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Stephan E Maier
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Radiology, Brigham Women's Hospital, Harvard Medical School Boston, Boston, Massachusetts, USA
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Kido A, Tamada T, Ueda Y, Takeuchi M, Kanki A, Yamamoto A. Comparison Between Amide Proton Transfer Magnetic Resonance Imaging Using 3-Dimensional Acquisition and Diffusion-Weighted Imaging for Characterization of Prostate Cancer: A Preliminary Study. J Comput Assist Tomogr 2023; 47:178-185. [PMID: 36729617 DOI: 10.1097/rct.0000000000001398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE This study aimed to compare diagnostic performance for tumor detection and for assessment of tumor aggressiveness in prostate cancer (PC) between amide proton transfer magnetic resonance imaging (MRI) with 3-dimensional acquisition (3DAPT) and diffusion-weighted imaging. METHODS The subjects were 23 patients with 27 pathologically proven PCs who underwent 3T multiparametric MRI. With reference to the pathology findings, 2 readers in consensus identified the location of PC on multiparametric MRI and measured APT signal intensity (APT SI [%]) and mean apparent diffusion coefficient (ADC) of the benign region and each PC lesion. RESULTS The mean ADC showed a significant difference between benign regions and PC lesions (0.74 ± 0.15 vs 1.37 ± 0.21, P < 0.001), whereas APT SI did not ( P = 0.091). Lesion APT SI was significantly higher and lesion ADC was significantly lower in PCs with Gleason group (GG) ≥3 than in PCs with GG ≤2 (3.37 ± 1.30 vs 1.78 ± 0.67, P < 0.001, and 0.71 ± 0.18 vs 0.79 ± 0.10, P = 0.038, respectively). The APT SI was significantly higher in GG3 than in GG1, in GG3 than in GG2, and in GG4 than in GG2 ( P = 0.009, P = 0.001, and P = 0.006, respectively). The area under the curve for separating tumor lesions and benign regions was 0.601 for 3DAPT and 0.983 for ADC ( P < 0.001). The area under the curve for separating tumors with GG ≤2 from tumors with GG ≥3 was 0.912 for 3DAPT and 0.734 for ADC ( P = 0.172). CONCLUSIONS In patients with PC, it might be preferable to use ADC to discriminate benign from malignant tissue and use APT SI for assessment of tumor aggressiveness.
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Affiliation(s)
- Ayumu Kido
- From the Department of Radiology, Kawasaki Medical School, Kurashiki
| | - Tsutomu Tamada
- From the Department of Radiology, Kawasaki Medical School, Kurashiki
| | | | | | - Akihiko Kanki
- From the Department of Radiology, Kawasaki Medical School, Kurashiki
| | - Akira Yamamoto
- From the Department of Radiology, Kawasaki Medical School, Kurashiki
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Comparison of Diffusion Kurtosis Imaging and Standard Mono-Exponential Apparent Diffusion Coefficient in Diagnosis of Significant Prostate Cancer-A Correlation with Gleason Score Assessed on Whole-Mount Histopathology Specimens. Diagnostics (Basel) 2023; 13:diagnostics13020173. [PMID: 36672983 PMCID: PMC9858256 DOI: 10.3390/diagnostics13020173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/29/2022] [Accepted: 01/01/2023] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The study was undertaken to compare the diagnostic performance of diffusion kurtosis imaging (DKI) with the standard monoexponential (ME) apparent diffusion coefficient (ADC) model in the detection of significant prostate cancer (PCa), using whole-mount histopathology of radical prostatectomy specimens as a reference standard. METHODS 155 patients with prostate cancer had undergone multiparametric magnetic resonance imaging (mpMRI) at 3T before prostatectomy. Quantitative diffusion parameters-the apparent diffusion coefficient corrected for non-Gaussian behavior (Dapp), kurtosis (K), ADC1200, and ADC2000 were correlated with Gleason score and compared between cancerous and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. RESULTS The mean values of all diffusion parameters (Dapp, K, ADC1200, ADC2000) were significantly different both between malignant and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. Although the kurtosis model was better fitted to DWI data, the diagnostic performance in receiver operating characteristic (ROC) analysis of DKI and the standard ADC model in the detection of significant PCa was similar in the peripheral zone (PZ) and in peripheral and transitional zones (TZ) together. In conclusion, our study was not able to demonstrate a clear superiority of the kurtosis model over standard ADC in the diagnosis of significant PCa in PZ and in both zones combined.
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Moradi S, Hashemi B, Bakhshandeh M, Banaei A, Mofid B. Introducing new plan evaluation indices for prostate dose painting IMRT plans based on apparent diffusion coefficient images. Radiat Oncol 2022; 17:193. [PMID: 36419067 PMCID: PMC9685857 DOI: 10.1186/s13014-022-02163-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Dose painting planning would be more complicated due to different levels of prescribed doses and more complex evaluation with conventional plan quality indices considering uniform dose prescription. Therefore, we tried to introduce new indices for evaluating the dose distribution conformity and homogeneity of treatment volumes based on the tumoral cell density and relative volumes of each lesion in prostate IMRT. METHODS CT and MRI scans of 20 male patients having local prostate cancer were used for IMRT DP planning. Apparent diffusion coefficient (ADC) images were imported to a MATLAB program to identify lesion regions based on ADC values automatically. Regions with ADC values lower than 750 mm2/s and regions with ADC values higher than 750 and less than 1500 mm2/s were considered CTV70Gy (clinical tumor volume with 70 Gy prescribed dose), and CTV60Gy, respectively. Other regions of the prostate were considered as CTV53Gy. New plan evaluation indices based on evaluating the homogeneity (IOE(H)), and conformity (IOE(C)) were introduced, considering the relative volume of each lesion and cellular density obtained from ADC images. These indices were compared with conventional homogeneity and conformity indices and IOEs without considering cellular density. Furthermore, tumor control probability (TCP) was calculated for each patient, and the relationship of the assessed indices were evaluated with TCP values. RESULTS IOE (H) and IOE (C) with considering cellular density had significantly lower values compared to conventional indices and IOEs without considering cellular density. (P < 0.05). TCP values had a stronger relationship with IOE(H) considering cell density (R2 = -0.415), and IOE(C) without considering cell density (R2 = 0.624). CONCLUSION IOE plan evaluation indices proposed in this study can be used for evaluating prostate IMRT dose painting plans. We suggested to consider cell densities in the IOE(H) calculation formula and it's appropriate to calculate IOE(C) without considering cell density values.
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Affiliation(s)
- Saman Moradi
- grid.412266.50000 0001 1781 3962Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, 1411713116 Iran
| | - Bijan Hashemi
- grid.412266.50000 0001 1781 3962Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, 1411713116 Iran
| | - Mohsen Bakhshandeh
- grid.411600.2Department of Radiology Technology, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, 1985717443 Iran
| | - Amin Banaei
- grid.412266.50000 0001 1781 3962Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, 1411713116 Iran
| | - Bahram Mofid
- grid.411600.2Department of Radiation Oncology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, 1985717443 Iran
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Numminen R, Montoya Perez I, Jambor I, Pahikkala T, Airola A. Quicksort leave-pair-out cross-validation for ROC curve analysis. Comput Stat 2022. [DOI: 10.1007/s00180-022-01288-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractReceiver Operating Characteristic (ROC) curve analysis and area under the ROC curve (AUC) are commonly used performance measures in diagnostic systems. In this work, we assume a setting, where a classifier is inferred from multivariate data to predict the diagnostic outcome for new cases. Cross-validation is a resampling method for estimating the prediction performance of a classifier on data not used for inferring it. Tournament leave-pair-out (TLPO) cross-validation has been shown to be better than other resampling methods at producing a ranking of data that can be used for estimating the ROC curves and areas under them. However, the time complexity of TLPOCV, $$O\left( n^2\right)$$
O
n
2
, means that it is impractical in many applications. In this article, a method called quicksort leave-pair-out cross-validation (QLPOCV) is presented in order to decrease the time complexity of obtaining a reliable ranking of data to $$O\left( n\log n\right)$$
O
n
log
n
. The proposed method is compared with existing ones in an experimental study, demonstrating that in terms of ROC curves and AUC values QLPOCV produces as accurate performance estimation as TLPOCV, outperforming both k-fold and leave-one-out cross-validation.
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8
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Granata V, Fusco R, Belli A, Danti G, Bicci E, Cutolo C, Petrillo A, Izzo F. Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when. Infect Agent Cancer 2022; 17:25. [PMID: 35681237 PMCID: PMC9185934 DOI: 10.1186/s13027-022-00441-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 05/30/2022] [Indexed: 12/13/2022] Open
Abstract
This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm2 and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy.
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
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Ueno Y, Tamada T, Sofue K, Murakami T. Diffusion and quantification of diffusion of prostate cancer. Br J Radiol 2022; 95:20210653. [PMID: 34538094 PMCID: PMC8978232 DOI: 10.1259/bjr.20210653] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
For assessing a cancer treatment, and for detecting and characterizing cancer, Diffusion-weighted imaging (DWI) is commonly used. The key in DWI's use extracranially has been due to the emergence of of high-gradient amplitude and multichannel coils, parallelimaging, and echo-planar imaging. The benefit has been fewer motion artefacts and high-quality prostate images.Recently, new techniques have been developed to improve the signal-to-noise ratio of DWI with fewer artefacts, allowing an increase in spatial resolution. For apparent diffusion coefficient quantification, non-Gaussian diffusion models have been proposed as additional tools for prostate cancer detection and evaluation of its aggressiveness. More recently, radiomics and machine learning for prostate magnetic resonance imaging have emerged as novel techniques for the non-invasive characterisation of prostate cancer. This review presents recent developments in prostate DWI and discusses its potential use in clinical practice.
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Affiliation(s)
- Yoshiko Ueno
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Tsutomu Tamada
- Departmentof Radiology, Kawasaki Medical School, Kurashiki, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
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Jambor I, Martini A, Falagario UG, Ettala O, Taimen P, Knaapila J, Syvänen KT, Steiner A, Verho J, Perez IM, Merisaari H, Vainio P, Lamminen T, Saunavaara J, Carrieri G, Boström PJ, Aronen HJ. How to read biparametric MRI in men with a clinical suspicious of prostate cancer: Pictorial review for beginners with public access to imaging, clinical and histopathological database. Acta Radiol Open 2021; 10:20584601211060707. [PMID: 34868663 PMCID: PMC8638086 DOI: 10.1177/20584601211060707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022] Open
Abstract
Prostate Magnetic Resonance Imaging (MRI) is increasingly being used in men with a clinical suspicion of prostate cancer (PCa). Performing prostate MRI without the use of an intravenous contrast (IV) agent in men with a clinical suspicion of PCa can lead to reduced MRI scan time. Enabling a large array of different medical providers (from mid-level to specialized radiologists) to evaluate and potentially report prostate MRI in men with a clinical suspicion of PCa with a high accuracy could be one way to enable wide adoption of prostate MRI in men with a clinical suspicion of PCa. The aim of this pictorial review is to provide an insight into acquisition, quality control and reporting of prostate MRI performed without IV contrast agent in men with a clinical suspicion of PCa, aimed specifically at radiologists starting reporting prostate MRI, urologists, urology/radiology residents and mid-level medical providers without experience in reporting prostate MRI. Free public access (http://petiv.utu.fi/improd/and http://petiv.utu.fi/multiimprod/) to complete datasets of 161 and 338 men is provided. The imaging datasets are accompanied by clinical, laboratory and histopathological findings. Several topics are simplified in order to provide a solid base for the development of skills needed for an unsupervised review and potential reporting of prostate MRI in men with a clinical suspicion of PCa. The current review represents the first step towards enabling a large array of different medical providers to review and report accurately prostate MRI performed without IV contrast agent in men with a clinical suspicion of PCa.
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Affiliation(s)
- Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
- Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Alberto Martini
- Department of Oncology/Unit of
Urology, Urological Research Institute, IRCCS
Ospedale San Raffaele, Milan, Italy
| | - Ugo G Falagario
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | - Otto Ettala
- Department of Urology, University of Turku and Turku
University Hospital, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku and Department of
Pathology, Turku University Hospital, Turku, Finland
| | - Juha Knaapila
- Department of Urology, University of Turku and Turku
University Hospital, Turku, Finland
| | - Kari T Syvänen
- Department of Urology, University of Turku and Turku
University Hospital, Turku, Finland
| | - Aida Steiner
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest
Finland, Turku University
Hospital, Turku, Finland
| | - Janne Verho
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest
Finland, Turku University
Hospital, Turku, Finland
| | - Ileana M Perez
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
- Turku Brain and Mind Center, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
- Turku Brain and Mind Center, University of Turku, Turku, Finland
| | - Paula Vainio
- Institute of Biomedicine, University of Turku and Department of
Pathology, Turku University Hospital, Turku, Finland
| | - Tarja Lamminen
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | - Jani Saunavaara
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
- Department of Medical Physics, Turku University
Hospital, Turku, Finland
| | - Giuseppe Carrieri
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | - Peter J Boström
- Department of Urology, University of Turku and Turku
University Hospital, Turku, Finland
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
- Department of Oncology/Unit of
Urology, Urological Research Institute, IRCCS
Ospedale San Raffaele, Milan, Italy
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11
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Diffusion-weighted imaging in prostate cancer. MAGMA (NEW YORK, N.Y.) 2021; 35:533-547. [PMID: 34491467 DOI: 10.1007/s10334-021-00957-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/11/2021] [Accepted: 08/29/2021] [Indexed: 12/21/2022]
Abstract
Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI (bpMRI), which incorporates only T2-weighted imaging and DWI, compared with mpMRI with dynamic contrast-enhanced MRI. Therefore, further optimization of DWI is essential to achieve clinical application of bpMRI for efficient detection of csPC in patients with elevated PSA levels. Although DWI acquisition is routinely performed using single-shot echo-planar imaging, this method suffers from such as susceptibility artifact and anatomic distortion, which remain to be solved. In this review article, we will outline existing problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.
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12
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Quantitative diffusion-weighted imaging and dynamic contrast-enhanced MR imaging for assessment of tumor aggressiveness in prostate cancer at 3T. Magn Reson Imaging 2021; 83:152-159. [PMID: 34454006 DOI: 10.1016/j.mri.2021.08.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 07/13/2021] [Accepted: 08/23/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To compare diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) for characterization of prostate cancer (PC). METHODS 104 PC patients who underwent prostate multiparametric MRI at 3T including DWI and DCE-MRI before MRI-guided biopsy or radical prostatectomy. Apparent diffusion coefficient (ADC) with histogram analysis (mean, 0-25th percentile, skewness, and kurtosis), intravoxel incoherent motion model including D and f; stretched exponential model including distributed diffusion coefficient (DDC) and a; and permeability parameters including Ktrans, Kep, and Ve were obtained from a region of interest placed on the dominant tumor of each patient. RESULTS ADCmean, ADC0-25, D, DDC, and Ve were significantly lower and Kep was significantly higher in GS ≥ 3 + 4 tumors (n = 89) than in GS = 3 + 3 tumors (n = 15), and also in GS ≥ 4 + 3 tumors (n = 57) than in GS ≤ 3 + 4 tumors (n = 47) (P < 0.001 to P = 0.040). f was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors (P = 0.022), but there was no significant difference between GS = 3 + 3 tumors and GS ≥ 3 + 4 tumors, or between the remaining metrics in both comparisons. In metrics with area under the curve (AUC) >0.80, there was a significant difference in AUC between ADC0-25 and D, and DDC for separating GS ≤ 3 + 4 tumors from GS ≥ 4 + 3 tumors (P = 0.040 and P = 0.022, respectively). There were no significant differences between metrics with AUC > 0.80 for separating GS = 3 + 3 tumors from GS ≥ 3 + 4 tumors. ADC0-25 had the highest correlation with Gleason grade (ρ = -0.625, P < 0.001). CONCLUSIONS DWI and DCE-MRI showed no apparent clinical superiority of non-Gaussian models or permeability MRI over the mono-exponential model for assessment of tumor aggressiveness in PC.
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13
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Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021; 65:612-626. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/18/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.
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Affiliation(s)
- Yu-Feng Wang
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Amy J Hayden
- Sydney West Radiation Oncology, Westmead Hospital, Wentworthville, New South Wales, Australia
- Faculty of Medicine, Western Sydney University, Sydney, New South Wales, Australia
- Faculty of Medicine, Health & Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
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14
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Park H, Kim SH, Lee Y, Son JH. Comparison of diagnostic performance between diffusion kurtosis imaging parameters and mono-exponential ADC for determination of clinically significant cancer in patients with prostate cancer. Abdom Radiol (NY) 2020; 45:4235-4243. [PMID: 32965517 DOI: 10.1007/s00261-020-02776-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/09/2020] [Accepted: 09/10/2020] [Indexed: 01/09/2023]
Abstract
PURPOSE To compare the diagnostic performance between diffusion kurtosis imaging (DKI) parameters and mono-exponential apparent diffusion coefficient (ADC) for determination of clinically significant cancer (CSC, Gleason score (GS) ≥ 7) in patients with histologically proven prostate cancer (PCa). METHODS A total of 92 patients (mean age: 71.5 years, range: 47-89 years) who had been diagnosed as PCa and undergone 3 T-MRI including DWI (b values, 0, 100, 1000, 2000s/mm2) were included in this study. The DKI parameters, namely apparent diffusion for non-Gaussian distribution (Dapp) and apparent kurtosis coefficient (Kapp), were calculated by dedicated software using mono-exponential and diffusion kurtosis models for quantitation. The measurement was performed for a whole tumor after segmentation, and pathologic topographic maps or systemic biopsy results served as the reference standard for segmentation. To compare the diagnostic performance of each parameter for determination of CSC, pair-wise comparison of receiver operating characteristic (ROC) curves was performed. RESULTS The study population consisted of GS 6 (n = 18), GS 7 (n = 31), GS 8 (n = 25), GS 9 (n = 15) and GS 10 (n = 3) patients. The area under the ROC curve of Kapp (0.707, 95% CI 0.603-0.798) for discriminating CSC from non-CSC was not significantly different from those of mono-exponential ADC (0.725, 0.622-0.813, P = 0.2175) or Dapp (0.726, 0.623-0.814, P = 0.9628). Diagnostic predictive values of Kapp were estimated to a maximum accuracy of 78%, a sensitivity of 86%, and a specificity of 47%, while those of mono-exponential ADC were 75, 81, and 53%, respectively. CONCLUSION The DKI parameters showed a diagnostic performance comparable to mono-exponential ADC for determination of CSC in patients with PCa.
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Affiliation(s)
- Hyungin Park
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Seung Ho Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea.
| | - Yedaun Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Jung Hee Son
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
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15
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Goodburn RJ, Barrett T, Patterson I, Gallagher FA, Lawrence EM, Gnanapragasam VJ, Kastner C, Priest AN. Removing rician bias in diffusional kurtosis of the prostate using real-data reconstruction. Magn Reson Med 2020; 83:2243-2252. [PMID: 31737935 PMCID: PMC7065237 DOI: 10.1002/mrm.28080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 10/22/2019] [Accepted: 10/23/2019] [Indexed: 12/24/2022]
Abstract
PURPOSE To compare prostate diffusional kurtosis imaging (DKI) metrics generated using phase-corrected real data with those generated using magnitude data with and without noise compensation (NC). METHODS Diffusion-weighted images were acquired at 3T in 16 prostate cancer patients, measuring 6 b-values (0-1500 s/mm2 ), each acquired with 6 signal averages along 3 diffusion directions, with noise-only images acquired to allow NC. In addition to conventional magnitude averaging, phase-corrected real data were averaged in an attempt to reduce rician noise-bias, with a range of phase-correction low-pass filter (LPF) sizes (8-128 pixels) tested. Each method was also tested using simulations. Pixelwise maps of apparent diffusion (D) and apparent kurtosis (K) were calculated for magnitude data with and without NC and phase-corrected real data. Average values were compared in tumor, normal transition zone (NTZ), and normal peripheral zone (NPZ). RESULTS Simulations indicated LPF size can strongly affect K metrics, where 64-pixel LPFs produced accurate metrics. Relative to metrics estimated from magnitude data without NC, median NC K were lower (P < 0.0001) by 6/11/8% in tumor/NPZ/NTZ, 64-LPF real-data K were lower (P < 0.0001) by 4/10/7%, respectively. CONCLUSION Compared with magnitude data with NC, phase-corrected real data can produce similar K, although the choice of phase-correction LPF should be chosen carefully.
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Affiliation(s)
- Rosie J. Goodburn
- Department of Medical PhysicsCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
- Division of Radiotherapy and ImagingThe Institute of Cancer ResearchLondon
| | - Tristan Barrett
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | - Ilse Patterson
- Department of RadiologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Ferdia A. Gallagher
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | - Edward M. Lawrence
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | | | - Christof Kastner
- Department of UrologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Andrew N. Priest
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
- Department of RadiologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
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16
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Kim E, Kim CK, Kim HS, Jang DP, Kim IY, Hwang J. Histogram analysis from stretched exponential model on diffusion-weighted imaging: evaluation of clinically significant prostate cancer. Br J Radiol 2020; 93:20190757. [PMID: 31899654 DOI: 10.1259/bjr.20190757] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE To evaluate the usefulness of histogram analysis of stretched exponential model (SEM) on diffusion-weighted imaging in evaluating clinically significant prostate cancer (CSC). METHODS A total of 85 patients with prostate cancer underwent 3 T multiparametric MRI, followed by radical prostatectomy. Histogram parameters of the tumor from the SEM [distributed diffusion coefficient (DDC) and α] and the monoexponential model [MEM; apparent diffusion coefficient (ADC)] were evaluated. The associations between parameters and Gleason score or Prostate Imaging Reporting and Data System v. 2 were evaluated. The area under the receiver operating characteristics curve was calculated to evaluate diagnostic performance of parameters in predicting CSC. RESULTS The values of histogram parameters of DDC and ADC were significantly lower in patients with CSC than in patients without CSC (p < 0.05), except for skewness and kurtosis. The value of the 25th percentile of α was significantly lower in patients with CSC than in patients without CSC (p = 0.014). Histogram parameters of ADC and DDC had significant weak to moderate negative associations with Gleason score or Prostate Imaging Reporting and Data System v. 2 (p < 0.001), except for skewness and kurtosis. For predicting CSC, the area under the curves of mean ADC (0.856), 50th percentile DDC (0.852), and 25th percentile α (0.707) yielded the highest values compared to other histogram parameters from each group. CONCLUSION Histogram analysis of the SEM on diffusion-weighted imaging may be a useful quantitative tool for evaluating CSC. However, the SEM did not outperform the MEM. ADVANCES IN KNOWLEDGE Histogram parameters of SEM may be useful for evaluating CSC.
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Affiliation(s)
- EunJu Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea.,Philips Healthcare, Seoul, Republic of Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Medical Device Management and Research, SAIHST Sungkyunkwan University, Seoul, Republic of Korea.,Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyun Soo Kim
- Department of Medicine, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Dong Pyo Jang
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - In Young Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
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17
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Brancato V, Cavaliere C, Salvatore M, Monti S. Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis. Sci Rep 2019; 9:16837. [PMID: 31728007 PMCID: PMC6856159 DOI: 10.1038/s41598-019-53350-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022] Open
Abstract
The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I2 statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.
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18
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Merisaari H, Taimen P, Shiradkar R, Ettala O, Pesola M, Saunavaara J, Boström PJ, Madabhushi A, Aronen HJ, Jambor I. Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magn Reson Med 2019; 83:2293-2309. [PMID: 31703155 DOI: 10.1002/mrm.28058] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 10/03/2019] [Accepted: 10/09/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate repeatability of prostate DWI-derived radiomics and machine learning methods for prostate cancer (PCa) characterization. METHODS A total of 112 patients with diagnosed PCa underwent 2 prostate MRI examinations (Scan1 and Scan2) performed on the same day. DWI was performed using 12 b-values (0-2000 s/mm2 ), post-processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance minimum redundancy, Spearman's ρ, Wilcoxon rank-sum) were evaluated in terms of Scan1-Scan2 repeatability using intraclass correlation coefficient (ICC)(3,1). Classification performance for clinically significant and insignificant PCa with Gleason grade groups 1 versus >1 was evaluated by area under the receiver operator characteristic curve in unseen random 30% data split. RESULTS The ICC(3,1) values for conventional radiomics and feature pruning methods were in the range of 0.28-0.90. The machine learning classifications varied between Scan1 and Scan2 with % of same class labels between Scan1 and Scan2 in the range of 61-81%. Surface-to-volume ratio and corner detector-based features were among the most represented features with high repeatability, ICC(3,1) >0.75, consistently high ranking using all 4 feature pruning methods, and classification performance with area under the receiver operator characteristic curve >0.70. CONCLUSION Surface-to-volume ratio and corner detectors for prostate DWI led to good classification of unseen data and performed similarly in Scan1 and Scan2 in contrast to multiple conventional radiomic features.
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Affiliation(s)
- Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Future Technologies, University of Turku, Turku, Finland.,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku, Turku, Finland.,Department of Pathology, Turku University Hospital, Turku, Finland
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Otto Ettala
- Department of Urology, University of Turku and Turku University hospital, Turku, Finland
| | - Marko Pesola
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Peter J Boström
- Department of Urology, University of Turku and Turku University hospital, Turku, Finland
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
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19
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Zhang P, Min X, Wang L, Feng Z, Ke Z, You H, Fan C, Li Q. Bi-exponential versus mono-exponential diffusion-weighted imaging for evaluating prostate cancer aggressiveness after radical prostatectomy: a whole-tumor histogram analysis. Acta Radiol 2019; 60:1566-1575. [PMID: 30897930 DOI: 10.1177/0284185119837932] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Peipei Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Xiangde Min
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Liang Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zhaoyan Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zan Ke
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Huijuan You
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Chanyuan Fan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Qiubai Li
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
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20
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Montoya Perez I, Jambor I, Pahikkala T, Airola A, Merisaari H, Saunavaara J, Alinezhad S, Väänänen RM, Tallgrén T, Verho J, Kiviniemi A, Ettala O, Knaapila J, Syvänen KT, Kallajoki M, Vainio P, Aronen HJ, Pettersson K, Boström PJ, Taimen P. Prostate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine-Learning Techniques. J Magn Reson Imaging 2019; 51:1540-1553. [PMID: 31588660 DOI: 10.1002/jmri.26945] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 09/09/2019] [Accepted: 09/09/2019] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Accurate risk stratification of men with a clinical suspicion of prostate cancer (cSPCa) remains challenging despite the increasing use of MRI. PURPOSE To evaluate the diagnostic accuracy of a unique biparametric MRI protocol (IMPROD bpMRI) combined with clinical and molecular markers in men with cSPCa. STUDY TYPE Prospective single-institutional clinical trial (NCT01864135). SUBJECTS Eighty men with cSPCa. FIELD STRENGTH/SEQUENCE 3T, surface array coils. Two T2 -weighted and three diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm2 ; 2) b-values 0,1500 s/mm2 ; 3) b-values 0, 2000 s/mm2 . ASSESSMENT IMPROD bpMRI examinations were qualitatively (IMPROD bpMRI Likert score) and quantitatively (DWI-based Gleason grade score) prospectively reported. Men with IMPROD bpMRI Likert 3-5 had two targeted biopsies followed by 12-core systematic biopsies (SB); those with IMPROD bpMRI Likert 1-2 had only SB. Additionally, 2-core from normal-appearing prostate areas were obtained for the mRNA expression of ACSM1, AMACR, CACNA1D, DLX1, PCA3, PLA2G7, RHOU, SPINK1, SPON2, TMPRSS2-ERG, and TDRD1 measured by quantitative reverse-transcription polymerase chain reaction. STATISTICAL TESTS Univariate and multivariate analysis using regularized least-squares, feature selection and tournament leave-pair-out cross-validation (TLPOCV), as well as 10 random splits of the data in training-testing sets, were used to evaluate the mRNA, clinical and IMPROD bpMRI parameters in detecting clinically significant prostate cancer (SPCa) defined as Gleason score ≥ 3 + 4. The evaluation metric was the area under the curve (AUC). RESULTS IMPROD bpMRI Likert demonstrated the highest TLPOCV AUC of 0.92. The tested clinical variables had AUC 0.56-0.73, while the mRNA and additional IMPROD bpMRI parameters had AUC 0.50-0.67 and 0.65-0.89 respectively. The combination of clinical and mRNA biomarkers produced TLPOCV AUC of 0.87, the highest TLPOCV performance without including IMPROD bpMRI Likert. DATA CONCLUSION The qualitative IMPROD bpMRI Likert score demonstrated the highest accuracy for SPCa detection compared with the tested clinical variables and mRNA biomarkers. LEVEL OF EVIDENCE 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1540-1553.
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Affiliation(s)
- Ileana Montoya Perez
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Future Technologies, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Tapio Pahikkala
- Department of Future Technologies, University of Turku, Turku, Finland
| | - Antti Airola
- Department of Future Technologies, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Future Technologies, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Jani Saunavaara
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Saeid Alinezhad
- Department of Biotechnology, University of Turku, Turku, Finland
| | | | - Terhi Tallgrén
- Department of Biotechnology, University of Turku, Turku, Finland
| | - Janne Verho
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Aida Kiviniemi
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Otto Ettala
- Department of Urology, University of Turku and Turku University hospital, Turku, Finland
| | - Juha Knaapila
- Department of Urology, University of Turku and Turku University hospital, Turku, Finland
| | - Kari T Syvänen
- Department of Urology, University of Turku and Turku University hospital, Turku, Finland
| | - Markku Kallajoki
- Institute of Biomedicine, University of Turku and Department of Pathology, Turku University Hospital, Turku, Finland
| | - Paula Vainio
- Institute of Biomedicine, University of Turku and Department of Pathology, Turku University Hospital, Turku, Finland
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Kim Pettersson
- Department of Biotechnology, University of Turku, Turku, Finland
| | - Peter J Boström
- Department of Urology, University of Turku and Turku University hospital, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku and Department of Pathology, Turku University Hospital, Turku, Finland
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21
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Di Trani MG, Nezzo M, Caporale AS, De Feo R, Miano R, Mauriello A, Bove P, Manenti G, Capuani S. Performance of Diffusion Kurtosis Imaging Versus Diffusion Tensor Imaging in Discriminating Between Benign Tissue, Low and High Gleason Grade Prostate Cancer. Acad Radiol 2019; 26:1328-1337. [PMID: 30545680 DOI: 10.1016/j.acra.2018.11.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 11/19/2018] [Accepted: 11/21/2018] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in discriminating benign tissue, low- and high-grade prostate adenocarcinoma (PCa). MATERIALS AND METHODS Forty-eight patients with biopsy-proven PCa of different Gleason grade (GG), who provided written informed consent, were enrolled. All subjects underwent 3T DWI examinations by using b values 0, 500, 1000, 1500, 2000, and 2500 s/mm2 and six gradient directions. Mean diffusivity, fractional anisotropy (FA), apparent kurtosis (K), apparent kurtosis-derived diffusivity (D), and proxy fractional kurtosis anisotropy (KFA) maps were obtained. Regions of interest were selected in PCa, in the contralateral benign zone, and in the peritumoral area. Histogram analysis was performed by measuring mean, 10th, 25th, and 90th (p90) percentile of the whole-lesion volume. Kruskal-Wallis test with Bonferroni correction was used to assess significant differences between different regions of interest. The correlation between diffusion metrics and GG and between DKI and DTI parameters was evaluated with Pearson's test. ROC curve analysis was carried out to analyze the ability of histogram variables to differentiate low- and high-GG PCa. RESULTS All metrics significantly discriminated PCa from benign and from peritumoral tissue (except for K, KFAp90, and FA). Kp90 showed the highest correlation with GG and the best diagnostic ability (area under the curve = 0.84) in discriminating low- from high-risk PCa. CONCLUSION Compared to DTI, DKI provides complementary and additional information about prostate cancer tissue, resulting more sensitive to PCa-derived modifications and more accurate in discriminating low- and high-risk PCa.
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Affiliation(s)
- Maria Giovanna Di Trani
- Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy; Department of Anatomical, Histological, Forensic and Locomotor System Science, Sapienza University of Rome, Via A. Scarpa 16, Rome 00161, Italy.
| | - Marco Nezzo
- Department of Diagnostic and Interventional Radiology, Molecular Imaging and Radiotherapy, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Alessandra S Caporale
- Department of Physics, CNR ISC, UOS Roma Sapienza, Sapienza University of Rome, Rome, Italy; Department of Radiology, University of Pennsylvania Hospital, Founders Pavilion, Philadelphia, Pennsylvania
| | - Riccardo De Feo
- Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy; Department of Physics, CNR ISC, UOS Roma Sapienza, Sapienza University of Rome, Rome, Italy
| | - Roberto Miano
- Urology Unit, Department of Experimental Medicine and Surgery, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Alessandro Mauriello
- Anatomic Pathology, Department of Experimental Medicine and Surgery, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Pierluigi Bove
- Urology Unit, Department of Experimental Medicine and Surgery, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Guglielmo Manenti
- Department of Diagnostic and Interventional Radiology, Molecular Imaging and Radiotherapy, PTV Foundation, Tor Vergata University of Rome, Rome, Italy
| | - Silvia Capuani
- Department of Physics, CNR ISC, UOS Roma Sapienza, Sapienza University of Rome, Rome, Italy
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22
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Toivonen J, Montoya Perez I, Movahedi P, Merisaari H, Pesola M, Taimen P, Boström PJ, Pohjankukka J, Kiviniemi A, Pahikkala T, Aronen HJ, Jambor I. Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization. PLoS One 2019; 14:e0217702. [PMID: 31283771 PMCID: PMC6613688 DOI: 10.1371/journal.pone.0217702] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/16/2019] [Indexed: 12/19/2022] Open
Abstract
Purpose To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2). Methods T2w, DWI (12 b values, 0–2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS. Results In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments. Conclusion Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.
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Affiliation(s)
- Jussi Toivonen
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
- * E-mail:
| | - Ileana Montoya Perez
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Parisa Movahedi
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Harri Merisaari
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
- Turku PET Centre, University of Turku, Turku, Finland
| | - Marko Pesola
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku and Dept. of Pathology, Turku University Hospital, Turku, Finland
| | | | | | - Aida Kiviniemi
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Tapio Pahikkala
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Hannu J. Aronen
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
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23
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Jambor I, Verho J, Ettala O, Knaapila J, Taimen P, Syvänen KT, Kiviniemi A, Kähkönen E, Perez IM, Seppänen M, Rannikko A, Oksanen O, Riikonen J, Vimpeli SM, Kauko T, Merisaari H, Kallajoki M, Mirtti T, Lamminen T, Saunavaara J, Aronen HJ, Boström PJ. Validation of IMPROD biparametric MRI in men with clinically suspected prostate cancer: A prospective multi-institutional trial. PLoS Med 2019; 16:e1002813. [PMID: 31158230 PMCID: PMC6546206 DOI: 10.1371/journal.pmed.1002813] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 04/25/2019] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) combined with targeted biopsy (TB) is increasingly used in men with clinically suspected prostate cancer (PCa), but the long acquisition times, high costs, and inter-center/reader variability of routine multiparametric prostate MRI limit its wider adoption. METHODS AND FINDINGS The aim was to validate a previously developed unique MRI acquisition and reporting protocol, IMPROD biparametric MRI (bpMRI) (NCT01864135), in men with a clinical suspicion of PCa in a multi-institutional trial (NCT02241122). IMPROD bpMRI has average acquisition time of 15 minutes (no endorectal coil, no intravenous contrast use) and consists of T2-weighted imaging and 3 separate diffusion-weighed imaging acquisitions. Between February 1, 2015, and March 31, 2017, 364 men with a clinical suspicion of PCa were enrolled at 4 institutions in Finland. Men with an equivocal to high suspicion (IMPROD bpMRI Likert score 3-5) of PCa had 2 TBs of up to 2 lesions followed by a systematic biopsy (SB). Men with a low to very low suspicion (IMPROD bpMRI Likert score 1-2) had only SB. All data and protocols are freely available. The primary outcome of the trial was diagnostic accuracy-including overall accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value-of IMPROD bpMRI for clinically significant PCa (SPCa), which was defined as a Gleason score ≥ 3 + 4 (Gleason grade group 2 or higher). In total, 338 (338/364, 93%) prospectively enrolled men completed the trial. The accuracy and NPV of IMPROD bpMRI for SPCa were 70% (113/161) and 95% (71/75) (95% CI 87%-98%), respectively. Restricting the biopsy to men with equivocal to highly suspicious IMPROD bpMRI findings would have resulted in a 22% (75/338) reduction in the number of men undergoing biopsy while missing 4 (3%, 4/146) men with SPCa. The main limitation is uncertainty about the true PCa prevalence in the study cohort, since some of the men may have PCa despite having negative biopsy findings. CONCLUSIONS IMPROD bpMRI demonstrated a high NPV for SPCa in men with a clinical suspicion of PCa in this prospective multi-institutional clinical trial. TRIAL REGISTRATION ClinicalTrials.gov NCT02241122.
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Affiliation(s)
- Ivan Jambor
- Department of Radiology, University of Turku, Turku, Finland
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Janne Verho
- Department of Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Otto Ettala
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Juha Knaapila
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Pathology, Turku University Hospital, Turku, Finland
| | - Kari T. Syvänen
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Aida Kiviniemi
- Department of Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Esa Kähkönen
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Ileana Montoya Perez
- Department of Radiology, University of Turku, Turku, Finland
- Department of Future Technologies, University of Turku, Turku, Finland
| | - Marjo Seppänen
- Department of Surgery, Satakunta Central Hospital, Pori, Finland
| | - Antti Rannikko
- Department of Urology, Helsinki University and Helsinki University Hospital, Helsinki, Finland
| | - Outi Oksanen
- Department of Radiology, Helsinki University Hospital, Helsinki, Finland
| | - Jarno Riikonen
- Department of Urology, Tampere University Hospital and University of Tampere, Tampere, Finland
| | | | - Tommi Kauko
- Department of Biostatistics, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Radiology, University of Turku, Turku, Finland
- Department of Future Technologies, University of Turku, Turku, Finland
| | - Markku Kallajoki
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Pathology, Turku University Hospital, Turku, Finland
| | - Tuomas Mirtti
- Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Tarja Lamminen
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Jani Saunavaara
- Department of Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Hannu J. Aronen
- Department of Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Peter J. Boström
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
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24
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Fedorov A, Schwier M, Clunie D, Herz C, Pieper S, Kikinis R, Tempany C, Fennessy F. An annotated test-retest collection of prostate multiparametric MRI. Sci Data 2018; 5:180281. [PMID: 30512014 PMCID: PMC6278692 DOI: 10.1038/sdata.2018.281] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 10/26/2018] [Indexed: 12/13/2022] Open
Abstract
Multiparametric Magnetic Resonance Imaging (mpMRI) is widely used for characterizing prostate cancer. Standard of care use of mpMRI in clinic relies on visual interpretation of the images by an expert. mpMRI is also increasingly used as a quantitative imaging biomarker of the disease. Little is known about repeatability of such quantitative measurements, and no test-retest datasets have been available publicly to support investigation of the technical characteristics of the MRI-based quantification in the prostate. Here we present an mpMRI dataset consisting of baseline and repeat prostate MRI exams for 15 subjects, manually annotated to define regions corresponding to lesions and anatomical structures, and accompanied by region-based measurements. This dataset aims to support further investigation of the repeatability of mpMRI-derived quantitative prostate measurements, study of the robustness and reliability of the automated analysis approaches, and to support development and validation of new image analysis techniques. The manuscript can also serve as an example of the use of DICOM for standardized encoding of the image annotation and quantification results.
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Affiliation(s)
- Andriy Fedorov
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Schwier
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Christian Herz
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Ron Kikinis
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Fraunhofer MEVIS, Bremen, Germany
- Mathematics/Computer Science Faculty, University of Bremen, Bremen, Germany
| | - Clare Tempany
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Fiona Fennessy
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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25
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Kuai ZX, Sang XQ, Yao YF, Chu CY, Zhu YM. Evaluation of non-monoexponential diffusion models for hepatocellular carcinoma using b values up to 2000 s/mm 2 : A short-term repeatability study. J Magn Reson Imaging 2018; 50:297-304. [PMID: 30447032 DOI: 10.1002/jmri.26563] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 10/15/2018] [Accepted: 10/16/2018] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Non-monoexponential diffusion models are being used increasingly for the characterization and curative effect evaluation of hepatocellular carcinoma (HCC). But the fitting quality of the models and the repeatability of their parameters have not been assessed for HCC. PURPOSE To evaluate kurtosis, stretched exponential, and statistical models for diffusion-weighted imaging (DWI) of HCC, using b-values up to 2000 s/mm2 , in terms of fitting quality and repeatability. STUDY TYPE Prospective. POPULATION Eighteen patients with HCC. FIELD STRENGTH/SEQUENCE Conventional and DW images (b = 0, 200, 500, 1000, 1500, 2000 s/mm2 ) were acquired at 3.0T. ASSESSMENT The parameters of the kurtosis, stretched exponential, and statistical models were calculated on regions of interest (ROIs) of each lesion. STATISTICAL TESTS The fitting quality was evaluated through comparing the fitting residuals produced on the average data of ROI between different models using a paired t-test or Wilcoxon rank-sum test. Repeatability of the fitted parameters at the median values on the voxelwise data of ROI was assessed using the within coefficient of variation (WCV), the intraclass correlation coefficient (ICC), and the 95% Bland-Altman limits of agreements (BA-LA). The repeatability was divided into four levels: excellent, good, acceptable, and poor, referring to the values of ICC and WCV. RESULTS Among three models, the stretched exponential model provided the best fit to HCC (P < 0.05), whereas the statistical model produced the largest fitting residuals (P < 0.05). The repeatability of K from the kurtosis model was excellent (ICC 0.915; WCV 8.79%), while the distributed diffusion coefficient (DDC) from the stretched model was just acceptable (ICC 0.477; WCV 27.83%). The repeatability was good for other diffusion-related parameters. DATA CONCLUSION Considering the model fit and repeatability, the kurtosis and stretched exponential models are the preferred models for the description of the DW signals of HCC with respect to the statistical model. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:297-304.
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Affiliation(s)
- Zi-Xiang Kuai
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, P.R. China
| | - Xi-Qiao Sang
- Division of Respiratory Disease, Fourth Hospital of Harbin Medical University, Harbin, 150001, China
| | - Yuan-Fei Yao
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, P.R. China
| | - Chun-Yu Chu
- College of Engineering, Bohai University, Jinzhou, P.R. China
| | - Yue-Min Zhu
- CREATIS, CNRS UMR 5220-INSERM U1206-University Lyon 1-INSA Lyon-University, Lyon, France
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26
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Montoya Perez I, Airola A, Boström PJ, Jambor I, Pahikkala T. Tournament leave-pair-out cross-validation for receiver operating characteristic analysis. Stat Methods Med Res 2018; 28:2975-2991. [PMID: 30126322 PMCID: PMC6745617 DOI: 10.1177/0962280218795190] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Receiver operating characteristic analysis is widely used for evaluating
diagnostic systems. Recent studies have shown that estimating an area under
receiver operating characteristic curve with standard cross-validation methods
suffers from a large bias. The leave-pair-out cross-validation has been shown to
correct this bias. However, while leave-pair-out produces an almost unbiased
estimate of area under receiver operating characteristic curve, it does not
provide a ranking of the data needed for plotting and analyzing the receiver
operating characteristic curve. In this study, we propose a new method called
tournament leave-pair-out cross-validation. This method extends leave-pair-out
by creating a tournament from pair comparisons to produce a ranking for the
data. Tournament leave-pair-out preserves the advantage of leave-pair-out for
estimating area under receiver operating characteristic curve, while it also
allows performing receiver operating characteristic analyses. We have shown
using both synthetic and real-world data that tournament leave-pair-out is as
reliable as leave-pair-out for area under receiver operating characteristic
curve estimation and confirmed the bias in leave-one-out cross-validation on
low-dimensional data. As a case study on receiver operating characteristic
analysis, we also evaluate how reliably sensitivity and specificity can be
estimated from tournament leave-pair-out receiver operating characteristic
curves.
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Affiliation(s)
- Ileana Montoya Perez
- Department of Future Technologies, University of Turku, Turku, Finland.,Department of Urology, Turku University Hospital, Turku, Finland
| | - Antti Airola
- Department of Future Technologies, University of Turku, Turku, Finland
| | - Peter J Boström
- Department of Urology, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, Turku University Hospital, Turku, Finland.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Tapio Pahikkala
- Department of Future Technologies, University of Turku, Turku, Finland
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27
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Ertas G. Estimating the distributed diffusion coefficient of breast tissue in diffusion-weighted imaging using multilayer perceptrons. Soft comput 2018. [DOI: 10.1007/s00500-018-3412-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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28
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Multiparametric Magnetic Resonance Imaging of the Prostate: Repeatability of Volume and Apparent Diffusion Coefficient Quantification. Invest Radiol 2018; 52:538-546. [PMID: 28463931 PMCID: PMC5544576 DOI: 10.1097/rli.0000000000000382] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the repeatability of a region of interest (ROI) volume and mean apparent diffusion coefficient (ADC) in standard-of-care 3 T multiparametric magnetic resonance imaging (mpMRI) of the prostate obtained with the use of endorectal coil. MATERIALS AND METHODS This prospective study was Health Insurance Portability and Accountability Act compliant, with institutional review board approval and written informed consent. Men with confirmed or suspected treatment-naive prostate cancer scheduled for mpMRI were offered a repeat mpMRI within 2 weeks. Regions of interest corresponding to the whole prostate gland, the entire peripheral zone (PZ), normal PZ, and suspected tumor ROI (tROI) on axial T2-weighted, dynamic contrast-enhanced subtract, and ADC images were annotated and assessed using Prostate Imaging Reporting and Data System (PI-RADS) v2. Repeatability of the ROI volume for each of the analyzed image types and mean ROI ADC was summarized with repeatability coefficient (RC) and RC%. RESULTS A total of 189 subjects were approached to participate in the study. Of 40 patients that gave initial agreement, 15 men underwent 2 mpMRI examinations and completed the study. Peripheral zone tROIs were identified in 11 subjects. Tumor ROI volume was less than 0.5 mL in 8 of 11 subjects. PI-RADS categories were identical between baseline-repeat studies in 11/15 subjects and differed by 1 point in 4/15. Peripheral zone tROI volume RC (RC%) was 233 mm (71%) on axial T2-weighted, 422 mm (112%) on ADC, and 488 mm (119%) on dynamic contrast-enhanced subtract. Apparent diffusion coefficient ROI mean RC (RC%) were 447 × 10 mm/s (42%) in PZ tROI and 471 × 10 mm/s (30%) in normal PZ. Significant difference in repeatability of the tROI volume across series was observed (P < 0.005). The mean ADC RC% was lower than volume RC% for tROI ADC (P < 0.05). CONCLUSIONS PI-RADS v2 overall assessment was highly repeatable. Multiparametric magnetic resonance imaging sequences differ in volume measurement repeatability. The mean tROI ADC is more repeatable compared with tROI volume in ADC. Repeatability of prostate ADC is comparable with that in other abdominal organs.
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29
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Liu W, Liu XH, Tang W, Gao HB, Zhou BN, Zhou LP. Histogram analysis of stretched-exponential and monoexponential diffusion-weighted imaging models for distinguishing low and intermediate/high gleason scores in prostate carcinoma. J Magn Reson Imaging 2018; 48:491-498. [PMID: 29412492 DOI: 10.1002/jmri.25958] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 01/12/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Noninvasive measures to evaluate the aggressiveness of prostate carcinoma (PCa) may benefit patients. PURPOSE To assess the value of stretched-exponential and monoexponential diffusion-weighted imaging (DWI) for predicting the aggressiveness of PCa. STUDY TYPE Retrospective study. SUBJECTS Seventy-five patients with PCa. FIELD STRENGTH 3T DWI examinations were performed using b-values of 0, 500, 1000, and 2000 s/mm2 . ASSESSMENT The research were based on entire-tumor histogram analysis and the reference standard was radical prostectomy. STATISTICAL TESTS The correlation analysis was programmed with Spearman's rank-order analysis between the histogram variables and Gleason grade group (GG). Receiver operating characteristic (ROC) regression was used to analyze the ability of these histogram variables to differentiate low-grade (LG) from intermediate/high-grade (HG) PCa. RESULTS The percentiles and mean of apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were correlated with GG (ρ: 0.414-0.593), while there was no significant relation among α value, skewnesses, and kurtosises with GG (ρ:0.034-0.323). HG tumors (ADC:484 ± 136, 592 ± 139, 670 ± 144, 788 ± 146, 895 ± 141 mm2 /s; DDC: 410 ± 142, 532 ± 172, 666 ± 193, 786 ± 196, 914 ± 181 mm2 /s) had lower values in the 10th , 25th , 50th , 75th percentiles and means than LG tumors (ADC: 644 ± 779, 737 ± 84, 836 ± 83, 919 ± 82, 997 ± 107 mm2 /s; DDC: 552 ± 82, 680 ± 94, 829 ± 112, 931 ± 106, 1045 ± 100 mm2 /s). However, there was no difference between LG and HG tumors in α value (0.671 ± 0.041 vs. 0.633 ± 0.114), kurtosises (ADC 0.09 vs. 0.086; DDC -0.033 vs. -0.317), or skewnesses (ADC -0.036 vs. 0.073; DDC -0.063 vs. 0.136). The above statistics were P < 0.01. ADC10 with AUC = 0.840 and DDC10 with AUC = 0.799 were similar in discriminating between LG and HG PCa at P < 0.05. DATA CONCLUSION Histogram variables of DDC and ADC may predict the aggressiveness of PCa, while α value does not. The abilities of ADC10 and DDC10 to discriminate LG from HG tumors were similar, and both better than their respective means. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:491-498.
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Affiliation(s)
- Wei Liu
- Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiao H Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hong B Gao
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bing N Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Liang P Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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30
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Godley KC, Syer TJ, Toms AP, Smith TO, Johnson G, Cameron D, Malcolm PN. Accuracy of high b-value diffusion-weighted MRI for prostate cancer detection: a meta-analysis. Acta Radiol 2018; 59:105-113. [PMID: 28376634 DOI: 10.1177/0284185117702181] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Background The diagnostic accuracy of diffusion-weighted imaging (DWI) to detect prostate cancer is well-established. DWI provides visual as well as quantitative means of detecting tumor, the apparent diffusion coefficient (ADC). Recently higher b-values have been used to improve DWI's diagnostic performance. Purpose To determine the diagnostic performance of high b-value DWI at detecting prostate cancer and whether quantifying ADC improves accuracy. Material and Methods A comprehensive literature search of published and unpublished databases was performed. Eligible studies had histopathologically proven prostate cancer, DWI sequences using b-values ≥ 1000 s/mm2, less than ten patients, and data for creating a 2 × 2 table. Study quality was assessed with QUADAS-2 (Quality Assessment of diagnostic Accuracy Studies). Sensitivity and specificity were calculated and tests for statistical heterogeneity and threshold effect performed. Results were plotted on a summary receiver operating characteristic curve (sROC) and the area under the curve (AUC) determined the diagnostic performance of high b-value DWI. Results Ten studies met eligibility criteria with 13 subsets of data available for analysis, including 522 patients. Pooled sensitivity and specificity were 0.59 (95% confidence interval [CI], 0.57-0.61) and 0.92 (95% CI, 0.91-0.92), respectively, and the sROC AUC was 0.92. Subgroup analysis showed a statistically significant ( P = 0.03) improvement in accuracy when using tumor visual assessment rather than ADC. Conclusion High b-value DWI gives good diagnostic performance for prostate cancer detection and visual assessment of tumor diffusion is significantly more accurate than ROI measurements of ADC.
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Affiliation(s)
- Keith Craig Godley
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | | | - Andoni Paul Toms
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
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31
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Jambor I. Optimization of prostate MRI acquisition and post-processing protocol: a pictorial review with access to acquisition protocols. Acta Radiol Open 2017; 6:2058460117745574. [PMID: 29242748 PMCID: PMC5724653 DOI: 10.1177/2058460117745574] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 11/07/2017] [Indexed: 12/31/2022] Open
Abstract
The aim of this review article is to provide insight into the optimization of 1.5-Testla (T) and 3-T prostate magnetic resonance imaging (MRI). An approach for optimization of data quantification, especially diffusion-weighted imaging (DWI), is provided. Benefits and limitations of various pulse sequences are discussed. Importable MRI protocols and access to imaging datasets is provided. Careful optimization of prostate MR acquisition protocol allows the acquisition of high-quality prostate MRI using clinical 1.5-T/3-T MR scanners with an overall acquisition time < 15 min.
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Affiliation(s)
- Ivan Jambor
- Department of Radiology, University of Turku, Turku, Finland
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32
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Liu C, Wang K, Li X, Zhang J, Ding J, Spuhler K, Duong T, Liang C, Huang C. Breast lesion characterization using whole-lesion histogram analysis with stretched-exponential diffusion model. J Magn Reson Imaging 2017; 47:1701-1710. [PMID: 29165847 DOI: 10.1002/jmri.25904] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 11/06/2017] [Indexed: 01/13/2023] Open
Affiliation(s)
- Chunling Liu
- Department of Radiology; Guangdong General Hospital affiliated to South China University of Technology/Guangdong Academy of Medical Sciences; P.R. China
| | - Kun Wang
- Department of Breast Center, Cancer Center; Guangdong General Hospital affiliated to South China University of Technology/Guangdong Academy of Medical Sciences; P.R. China
| | - Xiaodan Li
- Department of Radiology; Guangdong General Hospital affiliated to South China University of Technology/Guangdong Academy of Medical Sciences; P.R. China
| | - Jine Zhang
- Department of Radiology; Guangdong General Hospital affiliated to South China University of Technology/Guangdong Academy of Medical Sciences; P.R. China
| | - Jie Ding
- Department of Biomedical Engineering; Stony Brook University; Stony Brook New York USA
| | - Karl Spuhler
- Department of Biomedical Engineering; Stony Brook University; Stony Brook New York USA
| | - Timothy Duong
- Department of Radiology; Stony Brook Medicine; Stony Brook New York USA
| | - Changhong Liang
- Department of Radiology; Guangdong General Hospital affiliated to South China University of Technology/Guangdong Academy of Medical Sciences; P.R. China
| | - Chuan Huang
- Department of Radiology; Stony Brook Medicine; Stony Brook New York USA
- Department of Psychiatry; Stony Brook Medicine; Stony Brook New York USA
- Department of Biomedical Engineering; Stony Brook University; Stony Brook New York USA
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Jambor I, Kuisma A, Kähkönen E, Kemppainen J, Merisaari H, Eskola O, Teuho J, Perez IM, Pesola M, Aronen HJ, Boström PJ, Taimen P, Minn H. Prospective evaluation of 18F-FACBC PET/CT and PET/MRI versus multiparametric MRI in intermediate- to high-risk prostate cancer patients (FLUCIPRO trial). Eur J Nucl Med Mol Imaging 2017; 45:355-364. [PMID: 29147764 DOI: 10.1007/s00259-017-3875-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Accepted: 11/03/2017] [Indexed: 12/31/2022]
Abstract
PURPOSE The purpose of this study was to evaluate 18F-FACBC PET/CT, PET/MRI, and multiparametric MRI (mpMRI) in detection of primary prostate cancer (PCa). METHODS Twenty-six men with histologically confirmed PCa underwent PET/CT immediately after injection of 369 ± 10 MBq 18F-FACBC (fluciclovine) followed by PET/MRI started 55 ± 7 min from injection. Maximum standardized uptake values (SUVmax) were measured for both hybrid PET acquisitions. A separate mpMRI was acquired within a week of the PET scans. Logan plots were used to calculate volume of distribution (VT). The presence of PCa was estimated in 12 regions with radical prostatectomy findings as ground truth. For each imaging modality, area under the curve (AUC) for detection of PCa was determined to predict diagnostic performance. The clinical trial registration number is NCT02002455. RESULTS In the visual analysis, 164/312 (53%) regions contained PCa, and 41 tumor foci were identified. PET/CT demonstrated the highest sensitivity at 87% while its specificity was low at 56%. The AUC of both PET/MRI and mpMRI significantly (p < 0.01) outperformed that of PET/CT while no differences were detected between PET/MRI and mpMRI. SUVmax and VT of Gleason score (GS) >3 + 4 tumors were significantly (p < 0.05) higher than those for GS 3 + 3 and benign hyperplasia. A total of 442 lymph nodes were evaluable for staging, and PET/CT and PET/MRI demonstrated true-positive findings in only 1/7 patients with metastatic lymph nodes. CONCLUSIONS Quantitative 18F-FACBC imaging significantly correlated with GS but failed to outperform MRI in lesion detection. 18F-FACBC may assist in targeted biopsies in the setting of hybrid imaging with MRI.
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Affiliation(s)
- Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4-8, P.O. Box 52, FI-20521, Turku, Finland.
- Department of Radiology, University of Massachusetts Medical School - Baystate, Springfield, MA, USA.
- Turku PET Centre, Turku, Finland.
| | - Anna Kuisma
- Department of Oncology and Radiotherapy, Turku University Hospital, Turku, Finland
| | - Esa Kähkönen
- Department of Urology, Turku University Hospital, Turku, Finland
| | - Jukka Kemppainen
- Turku PET Centre, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4-8, P.O. Box 52, FI-20521, Turku, Finland
- Turku PET Centre, Turku, Finland
- Department of Information Technology, University of Turku, Turku, Finland
| | | | | | - Ileana Montoya Perez
- Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4-8, P.O. Box 52, FI-20521, Turku, Finland
- Department of Information Technology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Marko Pesola
- Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4-8, P.O. Box 52, FI-20521, Turku, Finland
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4-8, P.O. Box 52, FI-20521, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Peter J Boström
- Department of Urology, Turku University Hospital, Turku, Finland
| | - Pekka Taimen
- Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
| | - Heikki Minn
- Turku PET Centre, Turku, Finland
- Department of Oncology and Radiotherapy, Turku University Hospital, Turku, Finland
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Hectors SJ, Semaan S, Song C, Lewis S, Haines GK, Tewari A, Rastinehad AR, Taouli B. Advanced Diffusion-weighted Imaging Modeling for Prostate Cancer Characterization: Correlation with Quantitative Histopathologic Tumor Tissue Composition-A Hypothesis-generating Study. Radiology 2017; 286:918-928. [PMID: 29117481 DOI: 10.1148/radiol.2017170904] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Purpose To correlate quantitative diffusion-weighted imaging (DWI) parameters derived from conventional monoexponential DWI, stretched exponential DWI, diffusion kurtosis imaging (DKI), and diffusion-tensor imaging (DTI) with quantitative histopathologic tumor tissue composition in prostate cancer in a preliminary hypothesis-generating study. Materials and Methods This retrospective institutional review board-approved study included 24 patients with prostate cancer (mean age, 63 years) who underwent magnetic resonance (MR) imaging, including high-b-value DWI and DTI at 3.0 T, before prostatectomy. The following parameters were calculated in index tumors and nontumoral peripheral zone (PZ): apparent diffusion coefficient (ADC) obtained with monoexponential fit (ADCME), ADC obtained with stretched exponential modeling (ADCSE), anomalous exponent (α) obtained at stretched exponential DWI, ADC obtained with DKI modeling (ADCDKI), kurtosis with DKI, ADC obtained with DTI (ADCDTI), and fractional anisotropy (FA) at DTI. Parameters in prostate cancer and PZ were compared by using paired Student t tests. Pearson correlations between tumor DWI and quantitative histologic parameters (nuclear, cytoplasmic, cellular, stromal, luminal fractions) were determined. Results All DWI parameters were significantly different between prostate cancer and PZ (P < .012). ADCME, ADCSE, and ADCDKI all showed significant negative correlation with cytoplasmic and cellular fractions (r = -0.546 to -0.435; P < .034) and positive correlation with stromal fractions (r = 0.619-0.669; P < .001). ADCDTI and FA showed correlation only with stromal fraction (r = 0.512 and -0.413, respectively; P < .045). α did not correlate with histologic parameters, whereas kurtosis showed significant correlations with histopathologic parameters (r = 0.487, 0.485, -0.422 for cytoplasmic, cellular, and stromal fractions, respectively; P < .040). Conclusion Advanced DWI methods showed significant correlations with histopathologic tissue composition in prostate cancer. These findings should be validated in a larger study. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on November 10, 2017.
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Affiliation(s)
- Stefanie J Hectors
- From the Translational and Molecular Imaging Institute (S.J.H., S.S., S.L., B.T.) and Departments of Radiology (S.J.H., S.S., C.S., S.L., B.T.), Pathology (G.K.H.), and Urology (A.T., A.R.R.), Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029
| | - Sahar Semaan
- From the Translational and Molecular Imaging Institute (S.J.H., S.S., S.L., B.T.) and Departments of Radiology (S.J.H., S.S., C.S., S.L., B.T.), Pathology (G.K.H.), and Urology (A.T., A.R.R.), Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029
| | - Christopher Song
- From the Translational and Molecular Imaging Institute (S.J.H., S.S., S.L., B.T.) and Departments of Radiology (S.J.H., S.S., C.S., S.L., B.T.), Pathology (G.K.H.), and Urology (A.T., A.R.R.), Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029
| | - Sara Lewis
- From the Translational and Molecular Imaging Institute (S.J.H., S.S., S.L., B.T.) and Departments of Radiology (S.J.H., S.S., C.S., S.L., B.T.), Pathology (G.K.H.), and Urology (A.T., A.R.R.), Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029
| | - George K Haines
- From the Translational and Molecular Imaging Institute (S.J.H., S.S., S.L., B.T.) and Departments of Radiology (S.J.H., S.S., C.S., S.L., B.T.), Pathology (G.K.H.), and Urology (A.T., A.R.R.), Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029
| | - Ashutosh Tewari
- From the Translational and Molecular Imaging Institute (S.J.H., S.S., S.L., B.T.) and Departments of Radiology (S.J.H., S.S., C.S., S.L., B.T.), Pathology (G.K.H.), and Urology (A.T., A.R.R.), Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029
| | - Ardeshir R Rastinehad
- From the Translational and Molecular Imaging Institute (S.J.H., S.S., S.L., B.T.) and Departments of Radiology (S.J.H., S.S., C.S., S.L., B.T.), Pathology (G.K.H.), and Urology (A.T., A.R.R.), Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029
| | - Bachir Taouli
- From the Translational and Molecular Imaging Institute (S.J.H., S.S., S.L., B.T.) and Departments of Radiology (S.J.H., S.S., C.S., S.L., B.T.), Pathology (G.K.H.), and Urology (A.T., A.R.R.), Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY 10029
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Model selection for high b-value diffusion-weighted MRI of the prostate. Magn Reson Imaging 2017; 46:21-27. [PMID: 29031583 DOI: 10.1016/j.mri.2017.10.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 10/04/2017] [Accepted: 10/10/2017] [Indexed: 01/24/2023]
Abstract
PURPOSE To assess the abilities of the standard mono-exponential (ME), bi-exponential (BE), diffusion kurtosis (DK) and stretched exponential (SE) models to characterize diffusion signal in malignant and prostatic tissues and determine which of the four models best characterizes these tissues on a per-voxel basis. MATERIALS AND METHODS This institutional-review-board-approved, HIPAA-compliant, retrospective study included 55 patients (median age, 61years; range, 42-77years) with untreated, biopsy-proven PCa who underwent endorectal coil MRI at 3-Tesla, diffusion-weighted MRI acquired at eight b-values from 0 to 2000s/mm2. Estimated parameters were apparent diffusion coefficent (ME model); diffusion coefficients for the fast (Dfast) and slow (Dslow) components and fraction of fast component, ffast (BE model); diffusion coefficient D, and kurtosis K (DK model); distributed diffusion coefficient DDC and α for (SE model). For one region-of-interest (ROI) in PZ and another in PCa in each patient, the corrected Akaike information criterion (AICc) and the Akaike weight (w) were calculated for each voxel. RESULTS Based on AICc and w, all non-monoexponential models outperformed the ME model in PZ and PCa. The DK model in PZ and SE model in PCa ROIs best fit the greatest average percentages of voxels (39% and 43%, respectively) and had the highest mean w (35±16×10-2 and 41±22×10-2, respectively). CONCLUSION DK and SE models best fit DWI data in PZ and PCa, and non-ME models consistently outperformed the ME model. Voxel-wise mapping of the preferential model demonstrated that the vast majority of voxels in either tissue type were best fit with one of the non-monoexponential models. At the given SNR levels, the maximum b-value of 2000s/mm2 is not sufficiently high to identify the preferred non-monoexponential model.
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van Schie MA, Steenbergen P, Dinh CV, Ghobadi G, van Houdt PJ, Pos FJ, Heijmink SWTJP, van der Poel HG, Renisch S, Vik T, van der Heide UA. Repeatability of dose painting by numbers treatment planning in prostate cancer radiotherapy based on multiparametric magnetic resonance imaging. ACTA ACUST UNITED AC 2017; 62:5575-5588. [DOI: 10.1088/1361-6560/aa75b8] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Maurer MH, Heverhagen JT. Diffusion weighted imaging of the prostate-principles, application, and advances. Transl Androl Urol 2017; 6:490-498. [PMID: 28725591 PMCID: PMC5503962 DOI: 10.21037/tau.2017.05.06] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
This review article aims to provide an overview on the principles of diffusion-weighted magnetic resonance imaging (DW-MRI) and its applications in the imaging of the prostate. DW-MRI with regards to different applications for prostate cancer (PCa) detection and characterization, local staging as well as for active surveillance (AS) and tumor recurrence after radical prostatectomy (RP) will be discussed. Furthermore, advances in DW-MRI techniques like diffusion kurtosis imaging (DKI) will be presented.
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Affiliation(s)
- Martin H Maurer
- Department of Radiology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Johannes T Heverhagen
- Department of Radiology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
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Tamada T, Prabhu V, Li J, Babb JS, Taneja SS, Rosenkrantz AB. Prostate Cancer: Diffusion-weighted MR Imaging for Detection and Assessment of Aggressiveness-Comparison between Conventional and Kurtosis Models. Radiology 2017; 284:100-108. [PMID: 28394755 DOI: 10.1148/radiol.2017162321] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To compare standard diffusion-weighted (DW) imaging and diffusion kurtosis (DK) imaging for prostate cancer (PC) detection and characterization in a large patient cohort, with attention to the potential added value of DK imaging. Materials and Methods This retrospective institutional review board-approved study received a waiver of informed consent. Two hundred eighty-five patients with PC underwent 3.0-T phased-array coil prostate magnetic resonance (MR) imaging, including a DK imaging sequence (b values 0, 500, 1000, 1500, and 2000 sec/mm2) before prostatectomy. Maps of apparent diffusion coefficient (ADC) and diffusional kurtosis (K) were derived by using maximal b values of 1000 and 2000 sec/mm2, respectively. Mean ADC and K were obtained from volumes of interest (VOIs) placed on each patient's dominant tumor and benign prostate tissue. Metrics were compared between benign and malignant tissue, between Gleason score (GS) ≤ 3 + 3 and GS ≥ 3 + 4 tumors, and between GS ≤ 3 + 4 and GS ≥ 4 + 3 tumors by using paired t tests, analysis of variance, receiver operating characteristic (ROC) analysis, and exact tests. Results ADC and K showed significant differences for benign versus tumor tissues, GS ≤ 3 + 3 versus GS ≥ 3 + 4 tumors, and GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (P < .001 for all). ADC and K were highly correlated (r = -0.82; P < .001). Area under the ROC curve was significantly higher (P = .002) for ADC (0.921) than for K (0.902) for benign versus malignant tissue but was similar for GS ≤ 3 + 3 versus GS ≥ 3 + 4 tumors (0.715-0.744) and GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (0.694-0.720) (P > .15). ADC and K were concordant for these various outcomes in 80.0%-88.6% of patients; among patients with discordant results, ADC showed better performance than K for GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (P = .016) and was similar to K for other outcomes (P > .136). Conclusion ADC and K were highly correlated, had similar diagnostic performance, and were concordant for the various outcomes in the large majority of cases. These observations did not show a clear added value of DK imaging compared with standard DW imaging for clinical PC evaluation. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Tsutomu Tamada
- From the Department of Radiology (T.T., V.P., J.S.B., A.B.R.), Department of Pathology (J.L.), and Division of Urologic Oncology, Department of Urology (S.S.T., A.B.R.), NYU Langone Medical Center, 550 First Ave, New York, NY 10016
| | - Vinay Prabhu
- From the Department of Radiology (T.T., V.P., J.S.B., A.B.R.), Department of Pathology (J.L.), and Division of Urologic Oncology, Department of Urology (S.S.T., A.B.R.), NYU Langone Medical Center, 550 First Ave, New York, NY 10016
| | - Jianhong Li
- From the Department of Radiology (T.T., V.P., J.S.B., A.B.R.), Department of Pathology (J.L.), and Division of Urologic Oncology, Department of Urology (S.S.T., A.B.R.), NYU Langone Medical Center, 550 First Ave, New York, NY 10016
| | - James S Babb
- From the Department of Radiology (T.T., V.P., J.S.B., A.B.R.), Department of Pathology (J.L.), and Division of Urologic Oncology, Department of Urology (S.S.T., A.B.R.), NYU Langone Medical Center, 550 First Ave, New York, NY 10016
| | - Samir S Taneja
- From the Department of Radiology (T.T., V.P., J.S.B., A.B.R.), Department of Pathology (J.L.), and Division of Urologic Oncology, Department of Urology (S.S.T., A.B.R.), NYU Langone Medical Center, 550 First Ave, New York, NY 10016
| | - Andrew B Rosenkrantz
- From the Department of Radiology (T.T., V.P., J.S.B., A.B.R.), Department of Pathology (J.L.), and Division of Urologic Oncology, Department of Urology (S.S.T., A.B.R.), NYU Langone Medical Center, 550 First Ave, New York, NY 10016
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A comparative simulation study of bayesian fitting approaches to intravoxel incoherent motion modeling in diffusion-weighted MRI. Magn Reson Med 2017; 78:2373-2387. [DOI: 10.1002/mrm.26598] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 12/08/2016] [Accepted: 12/13/2016] [Indexed: 01/27/2023]
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Feng Z, Min X, Margolis DJA, Duan C, Chen Y, Sah VK, Chaudhary N, Li B, Ke Z, Zhang P, Wang L. Evaluation of different mathematical models and different b-value ranges of diffusion-weighted imaging in peripheral zone prostate cancer detection using b-value up to 4500 s/mm2. PLoS One 2017; 12:e0172127. [PMID: 28199367 PMCID: PMC5310778 DOI: 10.1371/journal.pone.0172127] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 01/31/2017] [Indexed: 01/29/2023] Open
Abstract
OBJECTIVES To evaluate the diagnostic performance of different mathematical models and different b-value ranges of diffusion-weighted imaging (DWI) in peripheral zone prostate cancer (PZ PCa) detection. METHODS Fifty-six patients with histologically proven PZ PCa who underwent DWI-magnetic resonance imaging (MRI) using 21 b-values (0-4500 s/mm2) were included. The mean signal intensities of the regions of interest (ROIs) placed in benign PZs and cancerous tissues on DWI images were fitted using mono-exponential, bi-exponential, stretched-exponential, and kurtosis models. The b-values were divided into four ranges: 0-1000, 0-2000, 0-3200, and 0-4500 s/mm2, grouped as A, B, C, and D, respectively. ADC, <D>, D*, f, DDC, α, Dapp, and Kapp were estimated for each group. The adjusted coefficient of determination (R2) was calculated to measure goodness-of-fit. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of the parameters. RESULTS All parameters except D* showed significant differences between cancerous tissues and benign PZs in each group. The area under the curve values (AUCs) of ADC were comparable in groups C and D (p = 0.980) and were significantly higher than those in groups A and B (p< 0.05 for all). The AUCs of ADC and Kapp in groups B and C were similar (p = 0.07 and p = 0.954), and were significantly higher than the other parameters (p< 0.001 for all). The AUCs of ADC in group D was slightly higher than Kapp (p = 0.002), and both were significantly higher than the other parameters (p< 0.001 for all). CONCLUSIONS ADC derived from conventional mono-exponential high b-value (3200 s/mm2) models is an optimal parameter for PZ PCa detection.
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Affiliation(s)
- Zhaoyan Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiangde Min
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Daniel J. A. Margolis
- Department of Radiology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, California, United States of America
| | - Caohui Duan
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, Hubei, China
| | - Yuping Chen
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Vivek Kumar Sah
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Nabin Chaudhary
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Basen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zan Ke
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Peipei Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Liang Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail:
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Fei B, Nieh PT, Master VA, Zhang Y, Osunkoya AO, Schuster DM. Molecular imaging and fusion targeted biopsy of the prostate. Clin Transl Imaging 2017; 5:29-43. [PMID: 28971090 PMCID: PMC5621648 DOI: 10.1007/s40336-016-0214-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 11/03/2016] [Indexed: 01/08/2023]
Abstract
PURPOSE This paper provides a review on molecular imaging with positron emission tomography (PET) and magnetic resonance imaging (MRI) for prostate cancer detection and its applications in fusion targeted biopsy of the prostate. METHODS Literature search was performed through the PubMed database using the keywords "prostate cancer", "MRI/ultrasound fusion", "molecular imaging", and "targeted biopsy". Estimates in autopsy studies indicate that 50% of men older than 50 years of age have prostate cancer. Systematic transrectal ultrasound (TRUS) guided prostate biopsy is considered the standard method for prostate cancer detection and has a significant sampling error and a low sensitivity. Molecular imaging technology and new biopsy approaches are emerging to improve the detection of prostate cancer. RESULTS Molecular imaging with PET and MRI shows promising results in the early detection of prostate cancer. MRI/TRUS fusion targeted biopsy has become a new clinical standard for the diagnosis of prostate cancer. PET molecular image-directed, three-dimensional ultrasound-guided biopsy is a new technology that has great potential for improving prostate cancer detection rate and for distinguishing aggressive prostate cancer from indolent disease. CONCLUSION Molecular imaging and fusion targeted biopsy are active research areas in prostate cancer research.
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Affiliation(s)
- Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of
Medicine, 1841 Clifton Road NE, Atlanta, GA 30329, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute
of Technology, Atlanta, GA 30329, USA
- Winship Cancer Institute of Emory University, Atlanta, GA 30329, USA
| | - Peter T. Nieh
- Department of Urology, Emory University School of Medicine, Atlanta, GA
30322, USA
| | - Viraj A. Master
- Department of Urology, Emory University School of Medicine, Atlanta, GA
30322, USA
| | - Yun Zhang
- Department of Radiology and Imaging Sciences, Emory University School of
Medicine, 1841 Clifton Road NE, Atlanta, GA 30329, USA
| | - Adeboye O. Osunkoya
- Winship Cancer Institute of Emory University, Atlanta, GA 30329, USA
- Department of Urology, Emory University School of Medicine, Atlanta, GA
30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of
Medicine, Atlanta, GA 30322, USA
- Department of Pathology, Veterans Affairs Medical Center, Decatur, GA 30033,
USA
| | - David M. Schuster
- Department of Radiology and Imaging Sciences, Emory University School of
Medicine, 1841 Clifton Road NE, Atlanta, GA 30329, USA
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Accelerating the Diffusion-Weighted Imaging Biomarker in the clinical practice: comparative study. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.procs.2017.05.108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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43
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Liu L, Tian Z, Zhang Z, Fei B. Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications. Acad Radiol 2016; 23:1024-46. [PMID: 27133005 PMCID: PMC5355004 DOI: 10.1016/j.acra.2016.03.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2015] [Revised: 03/18/2016] [Accepted: 03/21/2016] [Indexed: 01/10/2023]
Abstract
One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.
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Affiliation(s)
- Lizhi Liu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China
| | - Zhiqiang Tian
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329
| | - Zhenfeng Zhang
- Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1841 Clifton Road NE, Atlanta, Georgia 30329; Winship Cancer Institute of Emory University, 1841 Clifton Road NE, Atlanta, Georgia 30329.
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Lawrence EM, Warren AY, Priest AN, Barrett T, Goldman DA, Gill AB, Gnanapragasam VJ, Sala E, Gallagher FA. Evaluating Prostate Cancer Using Fractional Tissue Composition of Radical Prostatectomy Specimens and Pre-Operative Diffusional Kurtosis Magnetic Resonance Imaging. PLoS One 2016; 11:e0159652. [PMID: 27467064 PMCID: PMC4965080 DOI: 10.1371/journal.pone.0159652] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 07/05/2016] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Evaluating tissue heterogeneity using non-invasive imaging could potentially improve prostate cancer assessment and treatment. METHODS 20 patients with intermediate/high-risk prostate cancer underwent diffusion kurtosis imaging, including calculation of apparent diffusion (Dapp) and kurtosis (Kapp), prior to radical prostatectomy. Whole-mount tissue composition was quantified into: cellularity, luminal space, and fibromuscular stroma. Peripheral zone tumors were subdivided according to Gleason score. RESULTS Peripheral zone tumors had increased cellularity (p<0.0001), decreased fibromuscular stroma (p<0.05) and decreased luminal space (p<0.0001). Gleason score ≥4+3 tumors had significantly increased cellularity and decreased fibromuscular stroma compared to Gleason score ≤3+4 (p<0.05). In tumors, there was a significant positive correlation between median Kapp and cellularity (ρ = 0.50; p<0.05), and a negative correlation with fibromuscular stroma (ρ = -0.45; p<0.05). In normal tissue, median Dapp had a significant positive correlation with luminal space (ρ = 0.65; p<0.05) and a negative correlation with cellularity (ρ = -0.49; p<0.05). Median Kapp and Dapp varied significantly between tumor and normal tissue (p<0.0001), but only median Kapp was significantly different between Gleason score ≥4+3 and ≤3+4 (p<0.05). CONCLUSIONS Peripheral zone tumors have increased cellular heterogeneity which is reflected in mean Kapp, while normal prostate has a more homogeneous luminal space and cellularity better represented by Dapp.
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Affiliation(s)
- Edward M. Lawrence
- Department of Radiology, University of Cambridge, Addenbrooke’s hospital, Cambridge, United Kingdom
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, United States of America
| | - Anne Y. Warren
- Department of Histopathology, University of Cambridge, Addenbrooke’s hospital, Cambridge, United Kingdom
| | - Andrew N. Priest
- Department of Radiology, University of Cambridge, Addenbrooke’s hospital, Cambridge, United Kingdom
| | - Tristan Barrett
- Department of Radiology, University of Cambridge, Addenbrooke’s hospital, Cambridge, United Kingdom
| | - Debra A. Goldman
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, United States of America
| | - Andrew B. Gill
- Department of Radiology, University of Cambridge, Addenbrooke’s hospital, Cambridge, United Kingdom
| | - Vincent J. Gnanapragasam
- Department of Urology, University of Cambridge, Addenbrooke’s hospital, Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, United States of America
| | - Ferdia A. Gallagher
- Department of Radiology, University of Cambridge, Addenbrooke’s hospital, Cambridge, United Kingdom
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Merisaari H, Movahedi P, Perez IM, Toivonen J, Pesola M, Taimen P, Boström PJ, Pahikkala T, Kiviniemi A, Aronen HJ, Jambor I. Fitting methods for intravoxel incoherent motion imaging of prostate cancer on region of interest level: Repeatability and gleason score prediction. Magn Reson Med 2016; 77:1249-1264. [PMID: 26924552 DOI: 10.1002/mrm.26169] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 01/25/2016] [Accepted: 01/26/2016] [Indexed: 12/22/2022]
Abstract
PURPOSE To evaluate different fitting methods for intravoxel incoherent motion (IVIM) imaging of prostate cancer in the terms of repeatability and Gleason score prediction. METHODS Eighty-one patients with histologically confirmed prostate cancer underwent two repeated 3 Tesla diffusion-weighted imaging (DWI) examinations performed using 14 b-values in the range of 0-500 s/mm2 and diffusion time of 19.004 ms. Mean signal intensities of regions-of-interest were fitted using five different fitting methods for IVIM as well as monoexponential, kurtosis, and stretched exponential models. The fitting methods and models were evaluated in the terms of fitting quality [Akaike information criteria (AIC)], repeatability, and Gleason score prediction. Tumors were classified into three groups (3 + 3, 3 + 4, > 3 + 4). Machine learning algorithms were used to evaluate the performance of the combined use of the parameters. Simulation studies were performed to evaluate robustness of the fitting methods against noise. RESULTS Monoexponential model was preferred over IVIM based on AIC. The "pseudodiffusion" parameters demonstrated low repeatability and clinical value. Median "pseudodiffusion" fraction values were below 8.00%. Combined use of the parameters did not outperform the monoexponential model. CONCLUSION Monoexponential model demonstrated the highest repeatability and clinical values in the regions-of-interest based analysis of prostate cancer DWI, b-values in the range of 0-500 s/mm2 . Magn Reson Med 77:1249-1264, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Parisa Movahedi
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland.,Department of Information Technology, University of Turku, Turku, Finland
| | - Ileana M Perez
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland.,Department of Information Technology, University of Turku, Turku, Finland
| | - Jussi Toivonen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland.,Department of Information Technology, University of Turku, Turku, Finland
| | - Marko Pesola
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Pekka Taimen
- Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
| | - Peter J Boström
- Department of Urology, Turku University Hospital, Turku, Finland
| | - Tapio Pahikkala
- Department of Information Technology, University of Turku, Turku, Finland
| | - Aida Kiviniemi
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland.,Department of Information Technology, University of Turku, Turku, Finland
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Abstract
Multiple nonmorphologic magnetic resonance sequences are available in musculoskeletal imaging that can provide additional information to better characterize and diagnose musculoskeletal disorders and diseases. These sequences include blood-oxygen-level-dependent (BOLD), arterial spin labeling (ASL), diffusion-weighted imaging (DWI), and diffusion-tensor imaging (DTI). BOLD and ASL provide different methods to evaluate skeletal muscle microperfusion. The BOLD signal reflects the ratio between oxyhemoglobin and deoxyhemoglobin. ASL uses selective tagging of inflowing blood spins in a specific region for calculating local perfusion. DWI and DTI provide information about the structural integrity of soft tissue including muscles and fibers as well as pathologies.
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Jambor I, Pesola M, Merisaari H, Taimen P, Boström PJ, Liimatainen T, Aronen HJ. Relaxation along fictitious field, diffusion-weighted imaging, and T2 mapping of prostate cancer: Prediction of cancer aggressiveness. Magn Reson Med 2015; 75:2130-40. [PMID: 26094849 DOI: 10.1002/mrm.25808] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Revised: 05/20/2015] [Accepted: 05/21/2015] [Indexed: 12/13/2022]
Abstract
PURPOSE To evaluate the performance of relaxation along a fictitious field (RAFF) relaxation time (TRAFF ), diffusion-weighted imaging (DWI)-derived parameters, and T2 relaxation time values for prostate cancer (PCa) detection and characterization. METHODS Fifty patients underwent 3T MR examination using surface array coils before prostatectomy. DWI was performed using 14 and 12 b values in the ranges of 0-500 s/mm(2) and 0-2000 s/mm(2) , respectively. Repeated MR examination was performed in 16 patients. TRAFF , DWI-derived parameters (monoexponential, kurtosis, biexponential models), and T2 values were measured and averaged over regions of interest placed in PCa and normal tissue. Repeatability of TRAFF and DWI-derived parameters were assessed by coefficient of repeatability and intraclass correlation coefficient ICC(3,1). Areas under the receiver operating characteristic curve (AUCs) for PCa detection and Gleason score classification were estimated. The parameters were correlated with Gleason score groups using Spearman correlation coefficient (ρ). RESULTS ICC(3,1) values for TRAFF were in the range of 0.82-0.92. TRAFF values had higher AUC values for Gleason score classification compared with DWI-derived parameters and T2 . The RAFF method demonstrated the highest ρ value (-0.65). CONCLUSION In a quantitative region of interest-based analysis, RAFF outperformed DWI ("low" and "high" b values) and T2 mapping in the characterization of PCa.
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Affiliation(s)
- Ivan Jambor
- Department of Radiology, University of Turku, Turku, Finland
| | - Marko Pesola
- Department of Radiology, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Information Technology, University of Turku, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland
| | - Pekka Taimen
- Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
| | - Peter J Boström
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Timo Liimatainen
- Department of Biotechnology and Molecular Medicine, A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.,Imaging Centre, Kuopio University Hospital, Kuopio, Finland
| | - Hannu J Aronen
- Department of Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
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