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Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

ABSTRACT: PURPOSE:To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). MATERIALS AND METHODS:3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier. RESULTS:Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC?=?0.61-0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (P??0.14) were obtained for all institutions. CONCLUSION:A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ. LEVEL OF EVIDENCE:3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184-193.

SUBMITTER: Ginsburg SB

PROVIDER: S-EPMC5464994 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

Ginsburg Shoshana B SB Algohary Ahmad A Pahwa Shivani S Gulani Vikas V Ponsky Lee L Aronen Hannu J HJ Boström Peter J PJ Böhm Maret M Haynes Anne-Maree AM Brenner Phillip P Delprado Warick W Thompson James J Pulbrock Marley M Taimen Pekka P Villani Robert R Stricker Phillip P Rastinehad Ardeshir R AR Jambor Ivan I Madabhushi Anant A

Journal of magnetic resonance imaging : JMRI 20161219 1

<h4>Purpose</h4>To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ).<h4>Materials and methods</h4>3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved b ...[more]

PMID: 27990722

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Project description:BACKGROUND:Radiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa. PURPOSE:To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR. STUDY TYPE:Retrospective. SUBJECTS:In all, 120 PCa patients from two institutions, I1 and I2 , partitioned into training set D1 (N?=?70) from I1 and independent validation set D2 (N?=?50) from I2 . All patients were followed for ?3 years. SEQUENCE:3T, T2 -weighted (T2 WI) and apparent diffusion coefficient (ADC) maps derived from diffusion-weighted sequences. ASSESSMENT:PCa regions of interest (ROIs) on T2 WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T2 WI and ADC maps) were extracted from the ROIs. A machine-learning classifier (CBCR ) was trained with the best discriminating set of radiomic features to predict BCR (pBCR ). STATISTICAL TESTS:Wilcoxon rank-sum tests with P?<?0.05 were considered statistically significant. Differences in BCR-free survival at 3 years using pBCR was assessed using the Kaplan-Meier method and compared with Gleason Score (GS), PSA, and PIRADS-v2. RESULTS:Distribution statistics of co-occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T2 WI and ADC were associated with BCR (P?<?0.05) on D1 . CBCR predictions resulted in a mean AUC?=?0.84 on D1 and AUC?=?0.73 on D2 . A significant difference in BCR-free survival between the predicted classes (BCR?+?and BCR-) was observed (P?=?0.02) on D2 compared to those obtained from GS (P?=?0.8), PSA (P?=?0.93) and PIRADS-v2 (P?=?0.23). DATA CONCLUSION:Radiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy. LEVEL OF EVIDENCE:4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626-1636.

Project description:BackgroundMyxoid tumors pose diagnostic challenges for radiologists and pathologists. All myxoid tumors can be differentiated from each other using fluorescent in-situ hybridization (FISH) or immunohistochemical markers, except for myxomas and myxofibrosarcomas. Myxomas and myxofibrosarcomas are rare tumors. Myxomas are benign and histologically bland, whereas myxofibrosarcomas are malignant and histologically heterogenous. Because of the histological heterogeneity, low grade myxofibrosarcomas may be mistaken for myxomas on core needle biopsies. We evaluated the performance of T1-weighted signal intensity (T1SI), tumor volume, and radiomic features extracted from magnetic resonance imaging (MRI) to differentiate myxomas from myxofibrosarcomas.MethodsThe MRIs of 56 patients (29 with myxomas, 27 with myxofibrosarcomas) were analyzed. We extracted 89 radiomic features. Random forests based classifiers using the T1SI, volume features, and radiomic features were used to differentiate myxomas from myxofibrosarcomas. The classifiers were validated using a leave-one-out cross-validation. The performances of the classifiers were then compared.ResultsMyxomas had lower normalized T1SI than myxofibrosaromas (p = 0.006) and the AUC using the T1SI was 0.713. However, the classification model using radiomic features had an AUC of 0.885 (accuracy = 0.839, sensitivity = 0.852, specificity = 0.828), and outperformed the classification models using T1SI (AUC = 0.713) and tumor volume (AUC = 0.838). The classification model using radiomic features was significantly better than the classifier using T1SI values (p = 0.039).ConclusionsMyxofibrosarcomas are on average higher in T1-weighted signal intensity than myxomas. Myxofibrosarcomas are larger and have shape differences compared to myxomas. Radiomic features performed best for differentiating myxomas from myxofibrosarcomas compared to T1-weighted signal intensity and tumor volume features.

Project description:PurposeTo investigate reproducibility of myocardial radiomic features with cardiac MRI.Materials and methodsTest-retest studies were performed with a 3-T MRI system using commonly used cardiac MRI sequences of cine balanced steady-state free precession (cine bSSFP), T1-weighted and T2-weighted imaging, and quantitative T1 and T2 mapping in phantom experiments and 10 healthy participants (mean ± standard deviation age, 29 years ± 13). In addition, this study assessed repeatability in 51 patients (56 years ± 14) who underwent imaging twice during the same session. Three readers independently delineated the myocardium to investigate inter- and intraobserver reproducibility of radiomic features. A total of 1023 radiomic features were extracted by using PyRadiomics (https://pyradiomics.readthedocs.io/) with 11 image filters and six feature families. The intraclass correlation coefficient (ICC) was estimated to assess reproducibility and repeatability, and features with ICCs greater than or equal to 0.8 were considered reproducible.ResultsDifferent reproducibility patterns were observed among sequences in in vivo test-retest studies. In cine bSSFP, the gray-level run-length matrix was the most reproducible feature family, and the wavelet low-pass filter applied horizontally and vertically was the most reproducible image filter. In T1 and T2 maps, intensity-based statistics (first-order) and gray-level co-occurrence matrix features were the most reproducible feature families, without a dominant reproducible image filter. Across all sequences, gray-level nonuniformity was the most frequently identified reproducible feature name. In inter- and intraobserver reproducibility studies, respectively, only 32%-47% and 61%-73% of features were identified as reproducible.ConclusionOnly a small subset of myocardial radiomic features was reproducible, and these reproducible radiomic features varied among different sequences. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Leiner in this issue.

Project description:The use of multicentric data is becoming essential for developing generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI) data used in brain oncology are often heterogeneous in terms of scanners and acquisitions, which significantly impact quantitative radiomic features. Various methods have been proposed to decrease dependency, including methods acting directly on MR images, i.e., based on the application of several preprocessing steps before feature extraction or the ComBat method, which harmonizes radiomic features themselves. The ComBat method used for radiomics may be misleading and presents some limitations, such as the need to know the labels associated with the "batch effect". In addition, a statistically representative sample is required and the applicability of a signature whose batch label is not present in the train set is not possible. This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images. Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets.

Project description:The peripheral zone (PZ) and transition zone (TZ) represent about 70% of the human prostate gland with each zone having differential ability to develop prostate cancer. Androgens and their receptor are the primary driving cause of prostate cancer growth and eventually castration-resistant prostate cancer (CRPC). De novo steroidogenesis has been identified as a key mechanism that develops during CRPC. Currently, there is very limited information available on human prostate tissue steroidogenesis. The purpose of the present study was to investigate steroid metabolism in human prostate cancer tissues with comparison between PZ and TZ. Human prostate cancer tumors were procured from the patients who underwent radical prostatectomy without any neoadjuvant therapy. Human prostate homogenates were used to quantify steroid levels intrinsically present in the tissues as well as formed after incubation with 2 µg/mL of 17-hydroxypregnenolone (17-OH-pregnenolone) or progesterone. A Waters Acquity ultraperformance liquid chromatography coupled to a Quattro Premier XE tandem quadrupole mass spectrometer using a C18 column was used to measure thirteen steroids from the classical and backdoor steroidogenesis pathways. The intrinsic prostate tissue steroid levels were similar between PZ and TZ with dehydroepiandrosterone (DHEA), dihydrotestosterone (DHT), pregnenolone and 17-OH-pregnenolone levels higher than the other steroids measured. Interestingly, 5-pregnan-3,20-dione, 5-pregnan-3-ol-20-one, and 5-pregnan-17-ol-3,20-dione formation was significantly higher in both the zones of prostate tissues, whereas, androstenedione, testosterone, DHT, and progesterone levels were significantly lower after 60 min incubation compared to the 0 min control incubations. The incubations with progesterone had a similar outcome with 5-pregnan-3,20-dione and 5-pregnan-3-ol-20-one levels were elevated and the levels of DHT were lower in both PZ and TZ tissues. The net changes in steroid formation after the incubation were more observable with 17-OH-pregnenolone than with progesterone. In our knowledge, this is the first report of comprehensive analyses of intrinsic prostate tissue steroids and precursor-driven steroid metabolism using a sensitive liquid chromatography-mass spectrometry assay. In summary, the PZ and TZ of human prostate exhibited similar steroidogenic ability with distinction in the manner each zone utilizes the steroid precursors to divert the activity towards backdoor pathway through a complex matrix of steroidogenic mechanisms.

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Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

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Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

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