Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery.
about
Targeted prostate biopsy: value of multiparametric magnetic resonance imaging in detection of localized cancerQuantitative Evaluation of Treatment Related Changes on Multi-Parametric MRI after Laser Interstitial Thermal Therapy of Prostate Cancer.Texture Descriptors to distinguish Radiation Necrosis from Recurrent Brain Tumors on multi-parametric MRI.Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management.Prostatome: a combined anatomical and disease based MRI atlas of the prostate.Identifying Quantitative In Vivo Multi-Parametric MRI Features For Treatment Related Changes after Laser Interstitial Thermal Therapy of Prostate Cancer.Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomyFramework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model.Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings.Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.Class-specific weighting for Markov random field estimation: application to medical image segmentation.Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept StudyMulti-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRSSimultaneous Segmentation of Prostatic Zones Using Active Appearance Models With Multiple Coupled Levelsets.Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.Statistical 3D Prostate Imaging Atlas Construction via Anatomically Constrained RegistrationComputational imaging reveals shape differences between normal and malignant prostates on MRI.Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging.Multicenter validation of prostate tumor localization using multiparametric MRI and prior knowledge.Novel Imaging of Prostate Cancer with MRI, MRI/US, and PET.Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.The delineation of intraprostatic boost regions for radiotherapy using multimodality imaging.Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.In vivo MRI based prostate cancer localization with random forests and auto-context model.Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.Incorporating Oxygen-Enhanced MRI into Multi-Parametric Assessment of Human Prostate Cancer.Discriminative Scale Learning (DiScrn): Applications to Prostate Cancer Detection from MRI and Needle Biopsies.Molecular imaging and fusion targeted biopsy of the prostate.Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.Computer-aided diagnosis: detection and localization of prostate cancer within the peripheral zone.Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model.Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features.T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results.Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.Deep Learning Role in Early Diagnosis of Prostate Cancer.Computer-aided diagnosis of prostate cancer with MRIIdentifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings
P2860
Q27027567-6E3C0D6C-A7BF-41C5-BC7D-955391DA026BQ33578471-98767F53-ED28-4032-8739-3FC396AC048AQ33711485-5984631E-CFF1-45A1-80AE-AA8491824480Q33954963-29C88986-D6D3-4C81-9E2C-FFAE4412750AQ34296620-2EE2B858-8469-408F-924E-8BEB4552A69BQ34384735-622C8D38-74C7-4689-93D4-151F239331F9Q35686849-FD3F99E4-1616-4616-B640-AF643965FD32Q35907938-8AE15183-4AAE-4623-96BD-9B4D873154EFQ35921840-CEFE46B4-6E9E-4128-9C86-A2516DC6402AQ36306100-CD67504C-303F-4189-8902-215915B4EB50Q36425353-E605904D-7532-43E8-BBED-624A8A5A622EQ36429125-B8566FB1-A1E0-4A3D-8FD6-6B8A60DCFD13Q36804482-E4696CF8-385B-4876-926E-094574FE2624Q37003074-2860B03B-54A2-4F9A-92F7-75BCBA432445Q37130502-B844FAC4-6891-47CA-AF58-B44F783BB2E1Q37406144-EC43FFC5-B188-4D65-B2B9-43BD3BD63C9EQ37423607-8BE3601D-C008-4372-9284-2335EB6BA4E5Q37618298-0338E9B1-0C58-4DD8-B75D-75C2384A855BQ38163488-BB0E499C-FD01-4E6F-B886-E947FDC91D9EQ38403524-BFC65121-8A09-4D00-908E-4D21479A31D5Q38606252-C71C2B5D-37D8-4FF5-834C-10F50CE77255Q38822172-A44E649E-85F7-4383-974E-89F363633B34Q38851017-32C7523B-8CD1-4E62-B3BA-2D25A40D07DDQ39042948-7DF56BEF-2974-4ECF-86E5-EE34EBF5DC0EQ39860281-914F205D-25BE-4247-896B-6DBBBE31CDBFQ41110614-A412F4CA-C51C-4C6F-99B4-B1AC10BB646FQ41664708-B71ED820-6D5C-4C44-A8AE-0B71421E7D64Q42366450-B2CE9951-CFAB-4568-8C06-5EFC6BDEBF71Q47608215-7F500CEB-415C-4846-9A9D-2E5A6D53751DQ50422339-6A18755C-F755-4867-B25D-2911B0A1108CQ50654560-BCA52564-F4F4-46EA-86E3-61156293E181Q50862370-5A3E4B51-EDA1-4AC6-B2A9-A99E6C856B0EQ50906787-A278CE5C-564C-430E-A45B-5CCB83F45882Q51079664-8CE23594-7C47-4C86-9E82-4459D056A057Q52942612-9C89D576-FA02-411D-A069-27BDDDF63BC6Q52982100-E9CB7F9D-F70A-4586-9F71-6A14813FBE81Q55159859-A06C5DCD-61A3-4540-ACBC-028859EB7ABDQ57116404-38FFD18F-B126-4DFA-8372-2768C7A57FE1Q58782540-811B71F1-F042-45FC-BF53-703CEDE62A19
P2860
Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
2012年论文
@zh
2012年论文
@zh-cn
name
Central gland and peripheral z ...... n vivo T2-weighted MR imagery.
@ast
Central gland and peripheral z ...... n vivo T2-weighted MR imagery.
@en
type
label
Central gland and peripheral z ...... n vivo T2-weighted MR imagery.
@ast
Central gland and peripheral z ...... n vivo T2-weighted MR imagery.
@en
prefLabel
Central gland and peripheral z ...... n vivo T2-weighted MR imagery.
@ast
Central gland and peripheral z ...... n vivo T2-weighted MR imagery.
@en
P2093
P2860
P356
P1476
Central gland and peripheral z ...... n vivo T2-weighted MR imagery.
@en
P2093
Anant Madabhushi
Elizabeth M Genega
Jonathan C Chappelow
Neil M Rofsky
Nicholas B Bloch
Robert E Lenkinski
Robert Toth
Satish E Viswanath
P2860
P304
P356
10.1002/JMRI.23618
P577
2012-02-15T00:00:00Z