For a growing population of low- and intermediate-risk prostate cancer (CaP) patients, active surveillance may be biologically or psychologically undesirable. Yet the short- and long-term complications and co-morbidities associated with radical whole-organ therapies (e.g. intensity modulated radiation therapy) are still associated with a risk of treatment-related morbidity. With radical whole gland therapies, in certain patients who are more sensitive to radiation treatment, high dosage spots in the rectum and in the bladder can lead to complications including chronic rectal bleeding, diarrhea, and urinary symptoms such as cystitis. Laser induced interstitial thermal therapy (LITT) is a novel form of controlled, targeted thermal ablation that may offer measurable advantages over other ablative therapies for focal prostate therapy. Because LITT is MRI compatible, it enables an imaging advantage over other surgical or ablation techniques that utilize transrectal ultrasound to target and monitor treatment. However successful adoption of focal therapy for the treatment of CaP will hinge on several critical issues: 1) Can we accurately identify index lesions and cancers within the prostate? 2) Appropriate follow-up of patients treated with focal therapy and 3) How to detect recurrent/persistent disease? The introduction of multi-parametric (MP) MRI (T2w, dynamic contrast enhanced (DCE), Diffusion (DWI)) has allowed for (1) improved detection sensitivity and specificity for CaP localization, and (2) evaluating treatment response in the prostate. However there exists a need for (1) novel computational image analysis tools to quantitatively integrate MP-MRI parameters for improved CaP classification in vivo and (2) non-rigid registration tools for enabling targeted therapy and evaluation of post-treatment changes. In this study we will employ sophisticated computer vision, image analysis, computer assisted diagnostic (CAD) and deformable registration tools in conjunction with MP-MRI to be used in conjunction with a small clinical trial involving 40 patients with documented CaP for (a) automated delineation of tumor regions on pre-treatment MP-MRI to thereby identify the specific regions for ablation via LITT, and (b) identify and delineate locally recurrent disease within and outside the ablation zone for post-LITT evaluation. Regions identified via CAD on pre-LITT MRI will be targeted for therapy, while on post-LITT MP-MRI, regions identified as being suspicious for CaP recurrence (on account of large changes in MR imaging markers) will be evaluated via needle core biopsy. The tools developed in this project will be integrated into a practical and feasible treatment paradigm for focal treatment of low-risk localized CaP which will allow patients to avoid the complications associated with radical whole-gland therapy. This inter-disciplinary, translational project combines engineering expertise in terms of CAD on MP-MRI, multimodal image registration and machine learning and clinical expertise in interventional radiology, prostate MRI, and MRI guided focal therapy.
In this study we will leverage sophisticated computer vision, image analysis, computer assisted diagnostic and deformable registration tools in conjunction with multi-parametric (MP) MRI in prostate cancer (CaP) patients for (a) automated delineation of tumor regions on pre-treatment MP-MRI and thereby identify the specific regions for ablation via laser induced interstitial thermal therapy (LITT), and (b) identify and delineate locally recurrent disease within and outside the ablation zone for post-LITT evaluation. The tools developed in this project will be integrated into a feasible treatment paradigm for focal treatment of low-risk localized CaP which will allow patients to avoid the complications associated with radical whole-gland therapy.
|Lu, Cheng; Xu, Hongming; Xu, Jun et al. (2016) Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images. Sci Rep 6:33985|
|Prasanna, Prateek; Tiwari, Pallavi; Madabhushi, Anant (2016) Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor. Sci Rep 6:37241|
|Ginsburg, Shoshana B; Lee, George; Ali, Sahirzeeshan et al. (2016) Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology. IEEE Trans Med Imaging 35:76-88|
|Toth, Robert; Sperling, Dan; Madabhushi, Anant (2016) Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings. PLoS One 11:e0150016|
|Wan, Tao; Bloch, B Nicolas; Plecha, Donna et al. (2016) A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores. Sci Rep 6:21394|
|Sparks, Rachel; Madabhushi, Anant (2016) Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images. Sci Rep 6:27306|
|Janowczyk, Andrew; Basavanhally, Ajay; Madabhushi, Anant (2016) Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Comput Med Imaging Graph :|
|Tiwari, P; Prasanna, P; Wolansky, L et al. (2016) Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study. AJNR Am J Neuroradiol 37:2231-2236|
|Antunes, Jacob; Viswanath, Satish; Rusu, Mirabela et al. (2016) Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study. Transl Oncol 9:155-62|
|Litjens, Geert J S; Elliott, Robin; Shih, Natalie N C et al. (2016) Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging. Radiology 278:135-45|
Showing the most recent 10 out of 44 publications