Interobserver variation in tumor delineation of liver metastases using Magnetic Resonance Imaging.

Background and purpose: Magnetic Resonance Imaging (MRI) guided stereotactic body radiotherapy (SBRT) of liver metastases is an upcoming high-precision non-invasive treatment. Interobserver variation (IOV) in tumor delineation, however, remains a relevant uncertainty for planning target volume (PTV) margins. The aims of this study were to quantify IOV in MRI-based delineation of the gross tumor volume (GTV) of liver metastases and to detect patient-specific factors influencing IOV. Materials and methods: A total of 22 patients with liver metastases from three primary tumor origins were selected (colorectal(8), breast(6), lung(8)). Delineation guidelines and planning MRI-scans were provided to eight radiation oncologists who delineated all GTVs. All delineations were centrally peer reviewed to identify outliers not meeting the guidelines. Analyses were performed both in- and excluding outliers. IOV was quantified as the standard deviation (SD) of the perpendicular distance of each observer's delineation towards the median delineation. The correlation of IOV with shape regularity, tumor origin and volume was determined. Results: Including all delineations, average IOV was 1.6 mm (range 0.6-3.3 mm). From 160 delineations, in total fourteen single delineations were marked as outliers after peer review. After excluding outliers, the average IOV was 1.3 mm (range 0.6-2.3 mm). There was no significant correlation between IOV and tumor origin or volume. However, there was a significant correlation between IOV and regularity (Spearman's ρs = -0.66; p = 0.002). Conclusion: MRI-based IOV in tumor delineation of liver metastases was 1.3-1.6 mm, from which PTV margins for IOV can be calculated. Tumor regularity and IOV were significantly correlated, potentially allowing for patient-specific margin calculation.

Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer.

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications.

Metabolic Imaging for Radiation Therapy Treatment Planning: The Role of Hybrid PET/MR Imaging.

The use of hybrid PET/MR imaging for radiotherapy treatment planning has the potential to reduce tumor and organ displacements caused by different scan times and setup changes. Although with mixed results mainly due to single-center studies with small sample size, PET/MR imaging could provide better target delineation, especially by reducing coregistration discrepancies on computed tomography simulation scan and offering better soft tissue contrast. The main limitation to drive stronger conclusions is due to the relatively low availability of hybrid PET/MR imaging systems, mainly limited to large academic centers.

A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT.

Introduction: Accurate delineation of tumor targets is crucial for stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC). This study aims to develop a deep learning-based segmentation approach to accurately and efficiently delineate NSCLC targets using diagnostic PET-CT and SBRT planning CT (pCT). Methods: The diagnostic PET was registered to pCT using the transform matrix from registering diagnostic CT to the pCT. We proposed a 3D-UNet-based segmentation method to segment NSCLC tumor targets on dual-modality PET-pCT images. This network contained squeeze-and-excitation and Residual blocks in each convolutional block to perform dynamic channel-wise feature recalibration. Furthermore, up-sampling paths were added to supplement low-resolution features to the model and also to compute the overall loss function. The dice similarity coefficient (DSC), precision, recall, and the average symmetric surface distances were used to assess the performance of the proposed approach on 86 pairs of diagnostic PET and pCT images. The proposed model using dual-modality images was compared with both conventional 3D-UNet architecture and single-modality image input. Results: The average DSC of the proposed model with both PET and pCT images was 0.844, compared to 0.795 and 0.827, when using 3D-UNet and nnUnet. It also outperformed using either pCT or PET alone with the same network, which had DSC of 0.823 and 0.732, respectively. Discussion: Therefore, our proposed segmentation approach is able to outperform the current 3D-UNet network with diagnostic PET and pCT images. The integration of two image modalities helps improve segmentation accuracy.

A dataset of [68Ga]Ga-Pentixafor PET/CT images of patients with high-grade Glioma.

This paper contains single-center prospective information showing illustrative examples of chemokine receptor-4 (CXCR4) targeting in high-grade glial brain tumors in treatment-naïve adult patients using a novel radiolabeled PET tracer: [68Ga]Ga-CXCR4 PET/CT. High-grade glioma is one of the most resistant malignancies to treatment. Despite major breakthroughs in diagnostic and therapeutic approaches, the overall 5-year survival rate remains in the 5-10% range. CXCR4 is a chemokine with the C-X-C motif that is overexpressed in high-grade gliomas. The 24 consecutive treatment- naïve enrolled patients underwent PET/CT images using the SIEMENS scanner (Biograph6 TrueV) and received the radiotracer intravenously. After approximately 60 min, the PET/CT acquisition was performed with a dedicated scanner and in 10 min time per bed position. The images were reconstructed and analyzed with the 3D-OSEM algorithm, applying point spread function (PSF) or resolution recovery algorithm (TrueX in Syngo ® software, Siemens Medical Solution), 3 iterations, and 21 subsets using a 3 mm Gaussian post-smoothing filter. These data would be potentially beneficial for automatic tumor delineation machine learning after augmented with other data retrieved from different papers as well as for differentiation between an active viable tumor vs. post-surgery/necrosis in indeterminate cases. The theranostics potential (CXCR4-tageted labeled beta emitters) is one of the most novel areas of interest for future studies.

Cell-Membrane Coated Nanoparticles for Tumor Delineation and Qualitative Estimation of Cancer Biomarkers at Single Wavelength Excitation in Murine and Phantom Models.

Real-time guidance through fluorescence imaging improves the surgical outcomes of tumor resections, reducing the chances of leaving positive margins behind. As tumors are heterogeneous, it is imperative to interrogate multiple overexpressed cancer biomarkers with high sensitivity and specificity to improve surgical outcomes. However, for accurate tumor delineation and ratiometric detection of tumor biomarkers, current methods require multiple excitation wavelengths to image multiple biomarkers, which is impractical in a clinical setting. Here, we have developed a biomimetic platform comprising near-infrared fluorescent semiconducting polymer nanoparticles (SPNs) with red blood cell membrane (RBC) coating, capable of targeting two representative cell-surface biomarkers (folate, αυß3 integrins) using a single excitation wavelength for tumor delineation during surgical interventions. We evaluate our single excitation ratiometric nanoparticles in in vitro tumor cells, ex vivo tumor-mimicking phantoms, and in vivo mouse xenograft tumor models. Favorable biological properties (improved biocompatibility, prolonged blood circulation, reduced liver uptake) are complemented by superior spectral features: (i) specific fluorescence enhancement in tumor regions with high tumor-to-normal tissue (T/NT) ratios in ex vivo samples and (ii) estimation of cell-surface tumor biomarkers with single wavelength excitation providing insights about cancer progression (metastases). Our single excitation, dual output approach has the potential to differentiate between the tumor and healthy regions and simultaneously provide a qualitative indicator of cancer progression, thereby guiding surgeons in the operating room with the resection process.

Does consensus contours improve robustness and accuracy on [Formula: see text]F-FDG PET imaging tumor delineation?

PURPOSE: The aim of this study is to explore the robustness and accuracy of consensus contours with 225 nasopharyngeal carcinoma (NPC) clinical cases and 13 extended cardio-torso simulated lung tumors (XCAT) based on 2-deoxy-2-[[Formula: see text]F]fluoro-D-glucose ([Formula: see text]F-FDG) PET imaging. METHODS: Primary tumor segmentation was performed with two different initial masks on 225 NPC [Formula: see text]F-FDG PET datasets and 13 XCAT simulations using methods of automatic segmentation with active contour, affinity propagation (AP), contrast-oriented thresholding (ST), and 41% maximum tumor value (41MAX), respectively. Consensus contours (ConSeg) were subsequently generated based on the majority vote rule. The metabolically active tumor volume (MATV), relative volume error (RE), Dice similarity coefficient (DSC) and their respective test-retest (TRT) metrics between different masks were adopted to analyze the results quantitatively. The nonparametric Friedman and post hoc Wilcoxon tests with Bonferroni adjustment for multiple comparisons were performed with [Formula: see text] 0.05 considered to be significant. RESULTS: AP presented the highest variability for MATV in different masks, and ConSeg presented much better TRT performances in MATV compared with AP, and slightly poorer TRT in MATV compared with ST or 41MAXin most cases. Similar trends were also found in RE and DSC with the simulated data. The average of four segmentation results (AveSeg) showed better or comparable results in accuracy for most cases with respect to ConSeg. AP, AveSeg and ConSeg presented better RE and DSC in irregular masks as compared with rectangle masks. Additionally, all methods underestimated the tumour boundaries in relation to the ground truth for XCAT including respiratory motion. CONCLUSIONS: The consensus method could be a robust approach to alleviate segmentation variabilities, but did not seem to improve the accuracy of segmentation results on average. Irregular initial masks might be at least in some cases attributable to mitigate the segmentation variability as well.

Optical coherence tomography for presurgical delineation of basal cell carcinomas on the face-A comparison with histopathology.

BACKGROUND: To minimize the risk of incomplete excision of basal cell carcinomas (BCC) the macroscopic tumor margins should be adequately defined. Optical coherence tomography (OCT) is a non-invasive imaging tool that can provide structural and vascular information about skin cancer lesions. The study objective was to compare the presurgical delineation of facial BCC by clinical examination, histopathology, and OCT imaging in tumors undergoing full excision. METHODS: Ten patients with BCC lesions on the face were examined clinically, with OCT and histopathology at 3-mm intervals, from the clinical lesion border and beyond the resection line. The OCT scans were evaluated blinded and a delineation estimate of each BCC lesion was made. The results were compared to the clinical and histopathologic results. RESULTS: OCT evaluations and histopathology were in agreement in 86.6% of the collected data points. In three cases the OCT scans estimated a reduction of the tumor size compared to the clinical tumor border set by the surgeon. CONCLUSION: The results of this study support the notion that OCT can have a role in the clinical daily practice by aiding clinicians in delineating BCC lesions before surgery.

Improving the accuracy of motion quantification using area detector computed tomography for real-time tumor-tracking irradiation in stereotactic ablative radiotherapy.

CyberKnife radiotherapy enables tumor-tracking irradiation using positional information regarding the tumor and a fiducial marker in a patient's body. This positional information acts as a surrogate of tumor motion. Therefore, deviations in these movements should be quantitatively estimated and included as an internal margin for radiation treatment planning. This study aimed to investigate variations between the positions of fiducial markers and tumor regions using 320-row area detector computed tomography and to analyze the effectiveness of our proposed method in contouring tumor regions based on the fiducial marker position. To determine the moving tumor volume, a typical single-phase image was selected, and pixel values in other phase images were accumulated. Moreover, a maximum-intensity projection image was created to clarify motion deviations in the tumor volume. To evaluate the delineation accuracy, the dice similarity coefficient and mean distance to agreement were calculated in phase-selected and breath-holding computed tomography. Moving chest phantom images were acquired using helical scanning 4-dimensional computed tomography (H-4DCT) and volumetric scanning 4-dimensional computed tomography (V-4DCT), and the delineation accuracies were compared for each scanning type. The average dice similarity coefficient and mean distance to agreement were degraded in limited-phase images, which cannot represent the hysteretic motion of a tumor. Moreover, deviations in tumor volume with unstable motion reached 71.6% in H-4DCT but only 1.6% in V-4DCT. Our proposed method with V-4DCT using area detector computed tomography can achieve accurate moving tumor delineation and can clarify positional associations between the fiducial marker and tumor under respiratory motion.

Comparative Evaluation of 4-Dimensional Computed Tomography and 4-Dimensional Magnetic Resonance Imaging to Delineate the Target of Primary Liver Cancer.

Purpose: To evaluate the feasibility of 4-dimensional magnetic resonance imaging (4DMRI) in establishing the target of primary liver cancer in comparison with 4-dimensional computed tomography (4DCT). Methods and Materials: A total of 23 patients with primary liver cancer who received radiotherapy were selected, and 4DCT and T2w-4DMRI simulations were conducted to obtain 4DCT and T2w-4DMRI simulation images. The 4DCT and T2w-4DMRI data were sorted into 10 and 8 respiratory phase bins, respectively. The liver and gross tumor volumes (GTVs) were delineated in all images using programmed clinical workflows under tumor delineation guidelines. The internal organs at risk volumes (IRVs) and internal target volumes (ITVs) were the unions of all the phase livers and GTVs, respectively. Then, the artifacts, liver volume, GTV, and motion range in 4DCT and T2w-4DMRI were compared. Results: The mean GTV volume based on 4DMRI was 136.42 ± 231.27 cm3, which was 25.04 cm3 (15.5%) less than that of 4DCT (161.46 ± 280.29 cm3). The average volume of ITV determined by 4DMRI was 166.12 ± 270.43 cm3, which was 22.44 cm3 (11.9%) less than that determined by 4DCT (188.56 ± 307.57 cm3). Liver volume and IRV in 4DMRI increased by 4.0% and 6.6%, respectively, compared with 4DCT. The difference in tumor motion by T2w-4DMRI based on the centroid was greater than that of 4DCT in the L/R, A/P, and S/I directions, and the average displacement differences were 2.6, 2.8, and 6.9 mm, respectively. The severe artifacts in 4DCT were 47.8% (11/23) greater than in 4DMRI 17.4% (4/23). Conclusions: Compared with 4DCT, T2-weighted and navigator-triggered 4DMRI produces fewer artifacts and larger motion differences in hepatic intrafraction tumors, which is a feasible technique for primary liver cancer treatment planning.

Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions.

Fluorescence lifetime imaging (FLIm) is an optical spectroscopic imaging technique capable of real-time assessments of tissue properties in clinical settings. Label-free FLIm is sensitive to changes in tissue structure and biochemistry resulting from pathological conditions, thus providing optical contrast to identify and monitor the progression of disease. Technical and methodological advances over the last two decades have enabled the development of FLIm instrumentation for real-time, in situ, mesoscopic imaging compatible with standard clinical workflows. Herein, we review the fundamental working principles of mesoscopic FLIm, discuss the technical characteristics of current clinical FLIm instrumentation, highlight the most commonly used analytical methods to interpret fluorescence lifetime data and discuss the recent applications of FLIm in surgical oncology and cardiovascular diagnostics. Finally, we conclude with an outlook on the future directions of clinical FLIm.

Semiautomatic Tumor Delineation for Evaluation of 64Cu-DOTATATE PET/CT in Patients with Neuroendocrine Neoplasms: Prognostication Based on Lowest Lesion Uptake and Total Tumor Volume.

Patients with neuroendocrine neoplasms (NENs) have heterogeneous somatostatin receptor expression, with highly differentiated lesions having higher expression. Receptor expression of the total tumor burden may be visualized by somatostatin receptor imaging, such as with 64Cu-DOTATATE PET/CT. Assessment of maximal lesion uptake is associated with progression-free survival (PFS) but not overall survival (OS). We hypothesized that the lesion with the lowest, rather than the highest, 64Cu-DOTATATE uptake would be more prognostic, and we developed a semiautomatic method for evaluating this hypothesis. Methods: Patients with NENs underwent 64Cu-DOTATATE PET/CT. A standardized semiautomatic tumor delineation method was developed and used to identify the lesion with the lowest uptake, that is, with the lowest SUVmean Additionally, we assessed total tumor volume derived from the semiautomatic tumor delineation. Kaplan-Meier and Cox regression analyses were used to determine whether there was any association with OS and PFS. Results: In 116 patients with NENs, median PFS (95% CI) was 23 mo (range, 20-31 mo) and median OS was 85 mo (range, 68-113 mo). Minimum SUVmean and total tumor volume were significantly associated with PFS and OS in univariate Cox regression analyses, whereas SUVmax was significant only for PFS. In multivariate Cox analyses, both minimum SUVmean and total tumor volume remained statistically significant. Minimum SUVmean and total tumor volume were then dichotomized by their median, and patients were categorized into 4 groups: high or low total tumor volume and high or low minimum SUVmean Patients with a low total tumor volume and high minimum SUVmean had a hazard ratio of 0.32 (95% CI, 0.20-0.51) for PFS and 0.24 (95% CI, 0.13-0.43) for OS, both with P values of less than 0.001 (reference: high total tumor volume and low minimum SUVmean). Conclusion: We propose a standardized semiautomatic tumor delineation method to identify the lesion with the lowest 64Cu-DOTATATE uptake and total tumor volume. Assessment of the lowest, rather than the highest, lesion uptake greatly increases prognostication by 64Cu-DOTATATE PET/CT. Combining lesion uptake and total tumor volume, we derived a novel prognostic classification system for patients with NENs.

Convolutional neural network in nasopharyngeal carcinoma: how good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI?

PURPOSE: Convolutional neural networks (CNNs) show potential for delineating cancers on contrast-enhanced MRI (ce-MRI) but there are clinical scenarios in which administration of contrast is not desirable. We investigated performance of the CNN for delineating primary nasopharyngeal carcinoma (NPC) on non-contrast-enhanced images and compared the performance to that on ce-MRI. MATERIALS AND METHODS: We retrospectively analyzed primary NPC in 195 patients using a well-established CNN, U-Net, for tumor delineation on the non-contrast-enhanced fat-suppressed (fs)-T2W, ce-T1W and ce-fs-T1W images. The CNN-derived delineations were compared to manual delineations to obtain Dice similarity coefficient (DSC) and average surface distance (ASD). The DSC and ASD on fs-T2W were compared to those on ce-MRI. Primary tumor volumes (PTVs) of CNN-derived delineations were compared to that of manual delineations. RESULTS: The CNN for NPC delineation on fs-T2W images showed similar DSC (0.71 ± 0.09) and ASD (0.21 ± 0.48 cm) to those on ce-T1W images (0.71 ± 0.09 and 0.17 ± 0.19 cm, respectively) (p > 0.05), and lower DSC but similar ASD to ce-fs-T1W images (0.73 ± 0.09, p < 0.001; and 0.17 ± 0.20 cm, p > 0.05). The CNN overestimated PTVs on all sequences (p < 0.001). CONCLUSION: The CNN showed promise for NPC delineation on fs-T2W images in cases where it is desirable to avoid contrast agent injection. The CNN overestimated PTVs on all sequences.

Contribution of narrow band imaging in delineation of laryngopharyngeal superficial cancer spread: a prospective study.

PURPOSE: The aim of this study was to assess the performance of Narrow Band Imaging (NBI) added to White Light (WL) in the delineation of laryngopharyngeal superficial cancer spread during office-based transnasal flexible endoscopy. METHODS: This bi-centric prospective study was conducted between October 2014 and December 2017. We included consecutive patients with laryngopharyngeal malignant tumors. Transnasal flexible endoscopy was performed by two endoscopists who were blinded to each other's assessments and who examined each patient independently. The first endoscopist only performed a WL examination, while the second endoscopist carried out both WL and NBI. The extent of tumor involvement was reported based on predefined anatomical sub-units. Biopsies in NBI + /WL- sub-units were subsequently performed during panendoscopy. RESULTS: Eighty-four patients were included in the study. A total of 72 NBI + /WL- sub-units were sampled in 38 patients, and 37 of the biopsies were positive (51.4%): 16 for invasive carcinoma, 17 for high-grade dysplasia/carcinoma in situ and 4 for low-grade dysplasia. Ultimately, 26.2% of patients had at least one positive biopsy in an NBI + /WL- sub-unit and, therefore, a better tumor delineation. The clinical T stage was upgraded in 4.8% of cases examined. CONCLUSION: Adding NBI to WL imaging during transnasal flexible endoscopy in patients presenting with laryngopharyngeal pre-malignant or malignant lesions improves the delineation of superficial cancer spread, thereby leading to better adapted treatments. Clinicaltrials.gov registration number: NCT02035735.

Reduction of inter-observer contouring variability in daily clinical practice through a retrospective, evidence-based intervention.

BACKGROUND: Inter-observer variations (IOVs) arising during contouring can potentially impact plan quality and patient outcomes. Regular assessment of contouring IOV is not commonly performed in clinical practice due to the large time commitment required of clinicians from conventional methods. This work uses retrospective information from past treatment plans to facilitate a time-efficient, evidence-based intervention to reduce contouring IOV. METHODS: The contours of 492 prostate cancer treatment plans created by four radiation oncologists were analyzed in this study. Structure volumes, lengths, and DVHs were extracted from the treatment planning system and stratified based on primary oncologist and inclusion of a pelvic lymph node (PLN) target. Inter-observer variations and their dosimetric consequences were assessed using Student's t-tests. Results of this analysis were presented at an intervention meeting, where new consensus contour definitions were agreed upon. The impact of the intervention was assessed one-year later by repeating the analysis on 152 new plans. RESULTS: Significant IOV in prostate and PLN target delineation existed pre-intervention between oncologists, impacting dose to nearby OARs. IOV was also present for rectum and penile-bulb structures. Post-intervention, IOV decreased for all previously discordant structures. Dosimetric variations were also reduced. Although target contouring concordance increased significantly, some variations still persisted for PLN structures, highlighting remaining areas for improvement. CONCLUSION: We detected significant contouring IOV in routine practice using easily accessible retrospective data and successfully decreased IOV in our clinic through a reflective intervention. Continued application of this approach may aid improvements in practice standardization and enhance quality of care.

Label-free multiphoton imaging allows brain tumor recognition based on texture analysis-a study of 382 tumor patients.

BACKGROUND: Label-free multiphoton microscopy has been suggested for intraoperative recognition and delineation of brain tumors. For any future clinical application, appropriate approaches for image acquisition and analysis have to be developed. Moreover, an evaluation of the reliability of the approach, taking into account inter- and intrapatient variability, is needed. METHODS: Coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second-harmonic generation were acquired on cryosections of brain tumors of 382 patients and 28 human nontumor brain samples. Texture parameters of those images were calculated and used as input for linear discriminant analysis. RESULTS: The combined analysis of texture parameters of the CARS and TPEF signal proved to be most suited for the discrimination of nontumor brain versus brain tumors (low- and high-grade astrocytoma, oligodendroglioma, glioblastoma, recurrent glioblastoma, brain metastases of lung, colon, renal, and breast cancer and of malignant melanoma) leading to a correct rate of 96% (sensitivity: 96%, specificity: 100%). To approximate the clinical setting, the results were validated on 42 fresh, unfixed tumor biopsies. 82% of the tumors and, most important, all of the nontumor samples were correctly recognized. An image resolution of 1 µm was sufficient to distinguish brain tumors and nontumor brain. Moreover, the vast majority of single fields of view of each patient's sample were correctly classified with high probabilities, which is important for clinical translation. CONCLUSION: Label-free multiphoton imaging might allow fast and accurate intraoperative delineation of primary and secondary brain tumors in combination with endoscopic systems.

Histopathological Features of MRI-Invisible Regions of Prostate Cancer Lesions.

BACKGROUND: Previous studies have reported tumor volume underestimation with multiparametric (mp)MRI in prostate cancer diagnosis. PURPOSE: To investigate why some parts of lesions are not visible on mpMRI by comparing their histopathology features to those of visible regions. STUDY TYPE: Retrospective. POPULATION: Thirty-four patients with biopsy-proven prostate cancer scheduled for prostatectomy (median 68.7 years). FIELD STRENGTH/SEQUENCE: T2 -weighted, diffusion-weighted imaging, T2 mapping, and dynamic contrast-enhanced MRI on two 3T systems and one 1.5T system. ASSESSMENT: Two readers delineated suspicious lesions on mpMRI. A pathologist delineated the lesions on histopathology. A patient-customized mold enabled the registration of histopathology and MRI. On histopathology we identified mpMRI visible and invisible lesions. Subsequently, within the visible lesions we identified regions that were visible and regions that were invisible on mpMRI. For each lesion and region the following characteristics were determined: size, location, International Society of Urological Pathology (ISUP) grade, and Gleason subpatterns (density [dense/intermediate], tumor morphology [homogeneous/heterogeneous], cribriform growth [yes/no]). STATISTICAL TESTS: With generalized linear mixed-effect modeling we investigated which features explain why a lesion or a region was invisible on MRI. We compared imaging values (T2 , ADC, and Ktrans ) for these features with one-way analysis of variance (ANOVA). RESULTS: Small, anterior, and ISUP grade 1-2 lesions (n = 34) were missed more frequent than large, posterior, ISUP grade ≥ 3 lesions (n = 35). Invisible regions on mpMRI had lower tumor density, heterogeneous tumor morphology, and were located in the transition zone. Both T2 and ADC values were higher in "intermediate" compared with "dense" regions (P = 0.002 and < 0.001) and in regions with heterogeneous compared with homogeneous morphology (P < 0.001 and 0.03). Ktrans was not significantly different (P = 0.24 and 0.99). DATA CONCLUSION: Regions of prostate cancer lesions that are invisible on mpMRI have different histopathology features than visible regions. This may have implications for monitoring during active surveillance and focal treatment strategies. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1235-1246.

Clinical evaluation of 4D MRI in the delineation of gross and internal tumor volumes in comparison with 4DCT.

PURPOSE: To evaluate clinical utility of respiratory-correlated (RC) four-dimensional magnetic resonance imaging (4DMRI) for lung tumor delineation and motion assessment, in comparison with the current clinical standard of 4D computed tomography (4DCT). METHODS AND MATERIALS: A prospective T2-weighted (T2w) RC-4DMRI technique was applied to acquire coronal 4DMRI images for 14 lung cancer patients (16 lesions) during free breathing (FB) under an IRB-approved protocol, together with a breath-hold (BH) T1w 3DMRI and axial 4DMRI. Clinical simulation CT and 4DCT were acquired within 2 h. An internal navigator was applied to trigger amplitude-binned 4DMRI acquisition whereas a bellows or real-time position management (RPM) was used in the 4DCT reconstruction. Six radiation oncologists manually delineated the gross and internal tumor volumes (GTV and ITV) in 399 3D images using programmed clinical workflows under a tumor delineation guideline. The ITV was the union of GTVs within the breathing cycle without margin. Average GTV and motion range were assessed and ITV variation between 4DMRI and 4DCT was evaluated using the Dice similarity index, mean distance agreement (MDA), and volume difference. RESULTS: The mean tumor volume is similar between 4DCT (GTV4DCT  = 1.0, as the reference) and T2w-4DMRI (GTVT2wMR  = 0.97), but smaller in T1w MRI (GTVT1wMR  = 0.76), suggesting possible peripheral edema around the tumor. Average GTV variation within the breathing cycle (22%) in 4DMRI is slightly greater than 4DCT (17%). GTV motion variation (-4 to 12 mm) and ITV variation (∆VITV =-25 to 95%) between 4DCT and 4DMRI are large, confirmed by relatively low ITV similarity (Dice = 0.72 ± 0.11) and large MDA = 2.9 ± 1.5 mm. CONCLUSION: Average GTVs are similar between T2w-4DMRI and 4DCT, but smaller by 25% in T1w BH MRI. Physician training and breathing coaching may be necessary to reduce ITV variability between 4DMRI and 4DCT. Four-dimensional magnetic resonance imaging is a promising and viable technique for clinical lung tumor delineation and motion assessment.

Volumetric and Voxel-Wise Analysis of Dominant Intraprostatic Lesions on Multiparametric MRI.

Introduction: Multiparametric MR imaging (mpMRI) has shown promising results in the diagnosis and localization of prostate cancer. Furthermore, mpMRI may play an important role in identifying the dominant intraprostatic lesion (DIL) for radiotherapy boost. We sought to investigate the level of correlation between dominant tumor foci contoured on various mpMRI sequences. Methods: mpMRI data from 90 patients with MR-guided biopsy-proven prostate cancer were obtained from the SPIE-AAPM-NCI Prostate MR Classification Challenge. Each case consisted of T2-weighted (T2W), apparent diffusion coefficient (ADC), and Ktrans images computed from dynamic contrast-enhanced sequences. All image sets were rigidly co-registered, and the dominant tumor foci were identified and contoured for each MRI sequence. Hausdorff distance (HD), mean distance to agreement (MDA), and Dice and Jaccard coefficients were calculated between the contours for each pair of MRI sequences (i.e., T2 vs. ADC, T2 vs. Ktrans, and ADC vs. Ktrans). The voxel wise spearman correlation was also obtained between these image pairs. Results: The DILs were located in the anterior fibromuscular stroma, central zone, peripheral zone, and transition zone in 35.2, 5.6, 32.4, and 25.4% of patients, respectively. Gleason grade groups 1-5 represented 29.6, 40.8, 15.5, and 14.1% of the study population, respectively (with group grades 4 and 5 analyzed together). The mean contour volumes for the T2W images, and the ADC and Ktrans maps were 2.14 ± 2.1, 2.22 ± 2.2, and 1.84 ± 1.5 mL, respectively. Ktrans values were indistinguishable between cancerous regions and the rest of prostatic regions for 19 patients. The Dice coefficient and Jaccard index were 0.74 ± 0.13, 0.60 ± 0.15 for T2W-ADC and 0.61 ± 0.16, 0.46 ± 0.16 for T2W-Ktrans. The voxel-based Spearman correlations were 0.20 ± 0.20 for T2W-ADC and 0.13 ± 0.25 for T2W-Ktrans. Conclusions: The DIL contoured on T2W images had a high level of agreement with those contoured on ADC maps, but there was little to no quantitative correlation of these results with tumor location and Gleason grade group. Technical hurdles are yet to be solved for precision radiotherapy to target the DILs based on physiological imaging. A Boolean sum volume (BSV) incorporating all available MR sequences may be reasonable in delineating the DIL boost volume.

On the relation between MR spectroscopy features and the distance to MRI-visible solid tumor in GBM patients.

PURPOSE: To improve the detection of peritumoral changes in GBM patients by exploring the relation between MRSI information and the distance to the solid tumor volume (STV) defined using structural MRI (sMRI). METHODS: Twenty-three MRSI studies (PRESS, TE 135 ms) acquired from different patients with untreated GBM were used in this study. For each MRSI examination, the STV was identified by segmenting the corresponding sMRI images using BraTumIA, an automatic segmentation method. The relation between different metabolite ratios and the distance to STV was analyzed. A regression forest was trained to predict the distance from each voxel to STV based on 14 metabolite ratios. Then, the trained model was used to determine the expected distance to tumor (EDT) for each voxel of the MRSI test data. EDT maps were compared against sMRI segmentation. RESULTS: The features showing abnormal values at the longest distances to the tumor were: %NAA, Glx/NAA, Cho/NAA, and Cho/Cr. These four features were also the most important for the prediction of the distances to STV. Each EDT value was associated with a specific metabolic pattern, ranging from normal brain tissue to actively proliferating tumor and necrosis. Low EDT values were highly associated with malignant features such as elevated Cho/NAA and Cho/Cr. CONCLUSION: The proposed method enables the automatic detection of metabolic patterns associated with different distances to the STV border and may assist tumor delineation of infiltrative brain tumors such as GBM.