Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy

Abstract

Abstract. Laser interstitial thermotherapy (LITT) is a relatively new focal therapy technique for the ablation of localized prostate cancer. In this study, for the first time, we are integrating ex vivo pathology and magnetic resonance imaging (MRI) to assess the imaging characteristics of prostate cancer and treatment changes following LITT. Via a unique clinical trial, which gave us the availability of ex vivo histology and pre-and post-LITT MRIs, (1) we investigated the imaging characteristics of treatment effects and residual disease, and (2) evaluated treatment-induced feature changes in the ablated area relative to the residual disease. First, a pathologist annotated the ablated area and the residual disease on the ex vivo histology. Subsequently, we transferred the annotations to the post-LITT MRI using a semi-automatic elastic registration. The pre-and post-LITT MRIs were registered and features were extracted. A scoring metric based on the change in median pre-and post-LITT feature values was introduced, which allowed us to identify the most treatment responsive features. Our results show that (1) image characteristics for treatment effects and residual disease are different, and (2) the change of feature values between pre-and post-LITT MRIs can be a quantitative biomarker for treatment response. Finally, using feature change improved discrimination between the residual disease and treatment effects.

Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging

“…This finding is comparable to TREs reported in previous radiology-pathology studies, which implemented landmark-based registration (see Table 1). Notably, we achieved our results using only 5–7 anatomic landmarks (out of a total of 10–12 expert-identified landmarks) per patient to drive the registration for each patient, which is slightly below the average number of landmarks used in comparable studies (12,15). Although we could have used more landmarks to drive the registration, this would have resulted in very few landmarks being available to evaluate registration accuracy.…”

“…Litjens et al similarly employed a registration scheme to spatially map post-treatment pathologic annotations (necrosis and residual disease) onto postfocal therapy prostate MRI (15). However, as noted previously, corresponding MRI and pathology slices and anatomic landmarks are considerably easier to localize on the prostate.…”

“…The use of spatial coregistration methods for mapping pathologic annotations onto imaging has been previously performed by several groups, most popularly in the context of prostate cancer. Applications explored have included mapping disease extent onto diagnostic MRI (8,11–14), as well as mapping treatment effects and residual disease onto post-treatment MRI (15). …”

Classification of suspicious lesions on prostate multiparametric MRI using machine learning

“…5 Unlike PI-RADS, which largely depends on subjective assessment of mpMRI, computer-aided diagnosis (CAD) techniques for quantitative mpMRI analysis have also been developed for prostate cancer detection and diagnosis. [6][7][8][9][10][11][12] The core components in CAD systems for prostate cancer, as summarized by Liu et al 13 and Lemaitre et al, 14 include preprocessing of the images, segmentation of the prostate, image registration between MRI modalities, feature extraction, and voxel classification. The CAD efforts can be divided into two categories based on the main objectives for the analysis: (i) detection/segmentation of the suspicious lesion and/or (ii) assessment of the aggressiveness of prostate cancer.…”

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

“…This issue may be overcome by deriving computer-extracted features from radiographic images, termed radiomics [15] . Radiomic features attempt to capture “image texture” through quantification of local changes in image intensity values in relation to their voxel-wise arrangement [16] , [17] , [18] , [19] . This offers the ability to quantitatively capture subvisual “textural” changes in the tumor region, changes that may characterize early treatment response but escape visual identification.…”