Literature DB >> 25346574

Identifying Quantitative In Vivo Multi-Parametric MRI Features For Treatment Related Changes after Laser Interstitial Thermal Therapy of Prostate Cancer.

Satish Viswanath1, Robert Toth2, Mirabela Rusu1, Dan Sperling3, Herbert Lepor4, Jurgen Futterer5, Anant Madabhushi1.   

Abstract

Laser interstitial thermal therapy (LITT) is a new therapeutic strategy being explored in prostate cancer (CaP), which involves focal ablation of organlocalized tumor via an interstitial laser fiber. While little is known about treatment-related changes following LITT, studying post-LITT changes via imaging is extremely significant for enabling early image-guided intervention and follow-up. In this work, we present the first attempt at examining focal treatment-related changes on a per-voxel basis via quantitative comparison of MRI features pre- and post-LITT, and hence identifying computerized MRI features that are highly sensitive as well as specific to post-LITT changes within the ablation zone in the prostate. A retrospective cohort of 5 patient datasets comprising both pre- and post-LITT T2-weighted (T2w) and diffusion-weighted (DWI) acquisitions was considered, where DWI MRI yielded an Apparent Diffusion Co-efficient (ADC) map. Our scheme involved (1) inter-protocol registration of T2w and ADC MRI, as well as inter-acquisition registration of pre- and post-LITT MRI, (2) quantitation of MRI parameters by correcting for intensity drift in order to examine tissuespecific response, and (3) quantification of the information captured by T2w MRI and ADC maps via texture and intensity features. Correction of parameter drift resulted in visually discernible improvements in highlighting tissue-specific response in different MRI features. Quantitative, voxel-wise comparison of the changes in different MRI features indicated that steerable and non-steerable gradient texture features, rather than the original T2w intensity and ADC values, were highly sensitive as well as specific in identifying changes within the ablation zone pre- and post-LITT. The highest ranked texture feature yielded a normalized percentage change of 186% within the ablation zone and 43% in a spatially distinct normal region, relative to its pre-LITT value. By comparison, both the original T2w intensity and ADC value demonstrated a markedly less sensitive and specific response to changes within the ablation zone. Qualitative as well as quantitative evaluation of co-occurrence texture features indicated the presence of LITT-related effects such as edema adjacent to the ablation zone, which were indiscernible on the original T2w and ADC images. Our preliminary results thus indicate great potential for non-invasive computerized MRI imaging features for determining focal treatment related changes, informing image-guided interventions, as well as predicting long- and short-term patient outcome.

Entities:  

Keywords:  focal treatment; laser interstitial thermal therapy; multi-parametric MRI; prostate cancer; registration; treatment change; treatment evaluation

Year:  2014        PMID: 25346574      PMCID: PMC4205765          DOI: 10.1016/j.neucom.2014.03.065

Source DB:  PubMed          Journal:  Neurocomputing        ISSN: 0925-2312            Impact factor:   5.719


  27 in total

1.  MRI-guided thermal ablation therapy: model and parameter estimates to predict cell death from MR thermometry images.

Authors:  Michael S Breen; Miyuki Breen; Kim Butts; Lili Chen; Gerald M Saidel; David L Wilson
Journal:  Ann Biomed Eng       Date:  2007-04-07       Impact factor: 3.934

2.  Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information.

Authors:  Jonathan Chappelow; B Nicolas Bloch; Neil Rofsky; Elizabeth Genega; Robert Lenkinski; William DeWolf; Anant Madabhushi
Journal:  Med Phys       Date:  2011-04       Impact factor: 4.071

3.  Prostate cancer characterization on MR images using fractal features.

Authors:  R Lopes; A Ayache; N Makni; P Puech; A Villers; S Mordon; N Betrouni
Journal:  Med Phys       Date:  2011-01       Impact factor: 4.071

4.  Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery.

Authors:  Satish E Viswanath; Nicholas B Bloch; Jonathan C Chappelow; Robert Toth; Neil M Rofsky; Elizabeth M Genega; Robert E Lenkinski; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2012-02-15       Impact factor: 4.813

5.  Morphologic and clinical significance of multifocal prostate cancers in radical prostatectomy specimens.

Authors:  Alphaeus M Wise; Thomas A Stamey; John E McNeal; John L Clayton
Journal:  Urology       Date:  2002-08       Impact factor: 2.649

6.  T2-Weighted endorectal magnetic resonance imaging of prostate cancer after external beam radiation therapy.

Authors:  Antonio C Westphalen; John Kurhanewicz; Rui M G Cunha; I-Chow Hsu; John Kornak; Shoujun Zhao; Fergus V Coakley
Journal:  Int Braz J Urol       Date:  2009 Mar-Apr       Impact factor: 1.541

Review 7.  Focal therapy for localized prostate cancer: a critical appraisal of rationale and modalities.

Authors:  Scott E Eggener; Peter T Scardino; Peter R Carroll; Michael J Zelefsky; Oliver Sartor; Hedvig Hricak; Thomas M Wheeler; Samson W Fine; John Trachtenberg; Mark A Rubin; Mak Ohori; Kentaro Kuroiwa; Michel Rossignol; Lucien Abenhaim
Journal:  J Urol       Date:  2007-10-15       Impact factor: 7.450

8.  Elastic image registration for guiding focal laser ablation of prostate cancer: preliminary results.

Authors:  Nasr Makni; Philippe Puech; Pierre Colin; Abdelrahmene Azzouzi; Serge Mordon; Nacim Betrouni
Journal:  Comput Methods Programs Biomed       Date:  2012-05-09       Impact factor: 5.428

9.  Focal laser ablation of prostate cancer: definition, needs, and future.

Authors:  Pierre Colin; Serge Mordon; Pierre Nevoux; Mohammed Feras Marqa; Adil Ouzzane; Philippe Puech; Gregory Bozzini; Bertrand Leroux; Arnauld Villers; Nacim Betrouni
Journal:  Adv Urol       Date:  2012-05-16

10.  Focal laser ablation of prostate cancer: numerical simulation of temperature and damage distribution.

Authors:  Mohamad-Feras Marqa; Pierre Colin; Pierre Nevoux; Serge R Mordon; Nacim Betrouni
Journal:  Biomed Eng Online       Date:  2011-06-02       Impact factor: 2.819

View more
  7 in total

1.  Multimodal Imaging in Focal Therapy Planning and Assessment in Primary Prostate Cancer.

Authors:  Hossein Jadvar
Journal:  Clin Transl Imaging       Date:  2017-04-10

2.  Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings.

Authors:  Pallavi Tiwari; Shabbar F Danish; Benjamin Jiang; Anant Madabhushi
Journal:  J Med Imaging (Bellingham)       Date:  2015-09-25

3.  Radiomic Features of Primary Rectal Cancers on Baseline T2 -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study.

Authors:  Jacob T Antunes; Asya Ofshteyn; Kaustav Bera; Erik Y Wang; Justin T Brady; Joseph E Willis; Kenneth A Friedman; Eric L Marderstein; Matthew F Kalady; Sharon L Stein; Andrei S Purysko; Rajmohan Paspulati; Jayakrishna Gollamudi; Anant Madabhushi; Satish E Viswanath
Journal:  J Magn Reson Imaging       Date:  2020-03-26       Impact factor: 4.813

4.  Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management.

Authors:  Pallavi Tiwari; Shabbar Danish; Anant Madabhushi
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2014-03-12

5.  Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings.

Authors:  Robert Toth; Dan Sperling; Anant Madabhushi
Journal:  PLoS One       Date:  2016-04-18       Impact factor: 3.240

6.  Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study.

Authors:  Jacob Antunes; Satish Viswanath; Mirabela Rusu; Laia Valls; Christopher Hoimes; Norbert Avril; Anant Madabhushi
Journal:  Transl Oncol       Date:  2016-04       Impact factor: 4.243

Review 7.  Machine and deep learning methods for radiomics.

Authors:  Michele Avanzo; Lise Wei; Joseph Stancanello; Martin Vallières; Arvind Rao; Olivier Morin; Sarah A Mattonen; Issam El Naqa
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.