Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization.

SIGNIFICANCE: The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy. AIM: To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements. APPROACH: A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ (in vivo) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets. RESULTS: A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine). CONCLUSIONS: PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features.

SIGNIFICANCE: The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy. AIM: To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI). APPROACH: In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation. RESULTS: RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group = 1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity = 81 % and a specificity = 85 % ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1 / grade group <1 (accuracy = 87 % ) or grade group >1 / grade group ≤1 (accuracy = 91 % ). CONCLUSIONS: In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.

Performance of an integrated multimodality image guidance and dose-planning system supporting tumor-targeted HDR brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: Advances in high-dose-rate brachytherapy to treat prostate cancer hinge on improved accuracy in navigation and targeting while optimizing a streamlined workflow. Multimodal image registration and electromagnetic (EM) tracking are two technologies integrated into a prototype system in the early phase of clinical evaluation. We aim to report on the system's accuracy and workflow performance in support of tumor-targeted procedures. MATERIALS AND METHODS: In a prospective study, we evaluated the system in 43 consecutive procedures after clinical deployment. We measured workflow efficiency and EM catheter reconstruction accuracy. We also evaluated the system's MRI-TRUS registration accuracy with/without deformation, and with/without y-axis rotation for urethral alignment at initialization. RESULTS: The cohort included 32 focal brachytherapy and 11 integrated boost whole-gland implants. Mean procedure time excluding dose delivery was 38 min (range: 21-83) for focal, and 56 min (range: 38-89) for whole-gland implants; stable over time. EM catheter reconstructions achieved a mean difference between computed and measured free-length of 0.8 mm (SD 0.8, no corrections performed), and mean axial manual corrections 1.3 mm (SD 0.7). EM also enabled the clinical use of a non or partially visible catheter in 21% of procedures. Registration accuracy improved with y-axis rotation for urethral alignment at initialization and with the elastic registration (mTRE 3.42 mm, SD 1.49). CONCLUSION: The system supported tumor-targeting and was implemented with no demonstrable learning curve. EM reconstruction errors were small, correctable, and improved with calibration and control of external distortion sources; increasing confidence in the use of partially visible catheters. Image registration errors remained despite rotational alignment and deformation, and should be carefully considered.

High Sensitivity of Endoscopic Ultrasound-Guided Fine-Needle Aspiration and Endoscopic Ultrasound-Guided Fine-Needle Biopsy in Lymphadenopathy Caused by Metastatic Disease: A Prospective Comparative Study.

BACKGROUND/AIMS: The diagnostic work-up of lymphadenopathy is challenging but important to determine the correct therapy. Nevertheless, few studies have addressed the topic of endosonography (EUS)-guided tissue acquisition in lymphadenopathy. Therefore, we aimed to evaluate the accuracy and safety of EUS-guided fine-needle biopsy sampling (EUS-FNB) in intrathoracic and intraabdominal lymphadenopathy. METHODS: In a tertiary care center, patients with lymphadenopathy referred for EUS-guided sampling were included prospectively from 2014 to 2019 (NCT02360839). In all cases, EUS-FNB (22 gauge) and EUS-guided fine-needle aspiration (EUS-FNA) (25 gauge) were performed. The patients were randomized to the first needle pass with FNB or FNA. Study outcomes were the diagnostic accuracy and adverse event rate. RESULTS: Forty-eight patients were included (median age: 69 years [interquartile range, 59-76]; 24/48 females [50%]). The final diagnoses were metastasis (n=17), lymphoma (n=11), sarcoidosis (n=6), and inflammatory disease (n=14). The diagnostic performance of the two modalities was comparable, including a high sensitivity for metastatic nodes (EUS-FNB: 87% vs. EUSFNA: 100%, p=0.5). The sensitivity for lymphoma was borderline superior in favor of EUS-FNB (EUS-FNB: 55% vs. EUS-FNA: 9%, p=0.06). No adverse events were recorded. CONCLUSION: In lymphadenopathy, both EUS-FNB and EUS-FNA are safe and highly sensitive for metastatic lymph node detection. Lymphoma diagnosis is challenging regardless of the needle used.

Pre-clinical evaluation of an image-guided in-situ Raman spectroscopy navigation system for targeted prostate cancer interventions.

PURPOSE: Transrectal ultrasound (TRUS) image guidance is the standard of care for diagnostic and therapeutic interventions in prostate cancer (PCa) patients, but can lead to high false-negative rates, compromising downstream effectiveness of therapeutic choices. A promising approach to improve in-situ detection of PCa lies in using the optical properties of the tissue to discern cancer from healthy tissue. In this work, we present the first in-situ image-guided navigation system for a spatially tracked Raman spectroscopy probe integrated in a PCa workflow, capturing the optical tissue fingerprint. The probe is guided with fused TRUS/MR imaging and tested with both tissue-simulating phantoms and ex-vivo prostates. The workflow was designed to be integrated the clinical workflow for trans-perineal prostate biopsies, as well as for high-dose rate (HDR) brachytherapy. METHODS: The proposed system developed in 3D Slicer includes an electromagnetically tracked Raman spectroscopy probe, along with tracked TRUS imaging automatically registered to diagnostic MRI. The proposed system is tested on both custom gelatin tissue-simulating optical phantoms and biological tissue phantoms. A random-forest classifier was then trained on optical spectrums from ex-vivo prostates following prostatectomy using our optical probe. Preliminary in-human results are presented with the Raman spectroscopy instrument to detect malignant tissue in-situ with histopathology confirmation. RESULTS: In 5 synthetic gelatin and biological tissue phantoms, we demonstrate the ability of the image-guided Raman system by detecting over 95% of lesions, based on biopsy samples. The included lesion volumes ranged from 0.1 to 0.61 cc. We showed the compatibility of our workflow with the current HDR brachytherapy setup. In ex-vivo prostates of PCa patients, the system showed a 81% detection accuracy in high grade lesions. CONCLUSION: Pre-clinical experiments demonstrated promising results for in-situ confirmation of lesion locations in prostates using Raman spectroscopy, both in phantoms and human ex-vivo prostate tissue, which is required for integration in HDR brachytherapy procedures.

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge.

In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.

Computer-aided lateral ventricular and brain volume measurements in 3D ultrasound for assessing growth trajectories in newborns and neonates.

For newborns and neonates, ultrasound (US) is the most common imaging modality used for examinations due to its accessibility and ease of use. However, precise volume measurements remain limited in 2D, while MRI in newborns is typically avoided because of immobilization issues which may require sedation. The objective of this study is to assess and validate the lateral ventricular and total brain volumes obtained with an automatic segmentation method using cerebral trans-fontanelle 3D US. Infants aged between 2 and 8.5 months old were recruited, with both MRI and 3D US acquired on the same day was used to validate ventricular and brain volume measurements in comparison to MRI. Lateral ventricles were segmented on both the US (manually and with a proposed automatic fusion-based approach) and MRI, while brain volumes were estimated with an automatic segmentation method. Volumetric 3D US measurements were then evaluated with respect to age distribution. For the comparison between MRI and 3D US, strong inter-class correlations (ICC) were found for the ventricle volumes (manual: 5.9% ± 2.5% difference (ICC = 0.99); automatic: 6.0% ± 2.6% difference (ICC = 0.98)), as well as the total brain size, with a 3.0% ± 1.3% difference (ICC = 0.98). There was no statistically significant difference based on t-test and f-test for the lateral ventricles volume (t-test: p = 0.542) and (f-test: p = 0.738) and for the total brain volume (t-test: p = 0.412) and (f-test: p = 0.685) between MRI and 3D US. This study demonstrates that 3D US can be used to automatically assess lateral ventricular and total brain volumes with no significant difference to the MRI acquisitions. The highest correlations were obtained for infants under 8 months when the fontanelle is open.

Assessment of Rigid Registration Quality Measures in Ultrasound-Guided Radiotherapy.

Image guidance has become the standard of care for patient positioning in radiotherapy, where image registration is often a critical step to help manage patient motion. However, in practice, verification of registration quality is often adversely affected by difficulty in manual inspection of 3-D images and time constraint, thus affecting the therapeutic outcome. Therefore, we proposed to employ both bootstrapping and the supervised learning methods of linear discriminant analysis and random forest to help robustly assess registration quality in ultrasound-guided radiotherapy. We validated both approaches using phantom and real clinical ultrasound images, and showed that both performed well for the task. While learning-based techniques offer better accuracy and shorter evaluation time, bootstrapping requires no prior training and has a higher sensitivity.