Genome-wide loss of heterozygosity predicts aggressive, treatment-refractory behavior in pituitary neuroendocrine tumors.

Pituitary neuroendocrine tumors (PitNETs) exhibiting aggressive, treatment-refractory behavior are the rare subset that progress after surgery, conventional medical therapies, and an initial course of radiation and are characterized by unrelenting growth and/or metastatic dissemination. Two groups of patients with PitNETs were sequenced: a prospective group of patients (n = 66) who consented to sequencing prior to surgery and a retrospective group (n = 26) comprised of aggressive/higher risk PitNETs. A higher mutational burden and fraction of loss of heterozygosity (LOH) was found in the aggressive, treatment-refractory PitNETs compared to the benign tumors (p = 1.3 × 10-10 and p = 8.5 × 10-9, respectively). Within the corticotroph lineage, a characteristic pattern of recurrent chromosomal LOH in 12 specific chromosomes was associated with treatment-refractoriness (occurring in 11 of 14 treatment-refractory versus 1 of 14 benign corticotroph PitNETs, p = 1.7 × 10-4). Across the cohort, a higher fraction of LOH was identified in tumors with TP53 mutations (p = 3.3 × 10-8). A machine learning approach identified loss of heterozygosity as the most predictive variable for aggressive, treatment-refractory behavior, outperforming the most common gene-level alteration, TP53, with an accuracy of 0.88 (95% CI: 0.70-0.96). Aggressive, treatment-refractory PitNETs are characterized by significant aneuploidy due to widespread chromosomal LOH, most prominently in the corticotroph tumors. This LOH predicts treatment-refractoriness with high accuracy and represents a novel biomarker for this poorly defined PitNET category.

T1-Weighted, Dynamic Contrast-Enhanced MR Perfusion Imaging Can Differentiate between Treatment Success and Failure in Spine Metastases Undergoing Radiation Therapy.

BACKGROUND AND PURPOSE: Current imaging techniques have difficulty differentiating treatment success and failure in spinal metastases undergoing radiation therapy. This study investigated the correlation between changes in dynamic contrast-enhanced MR imaging perfusion parameters and clinical outcomes following radiation therapy for spinal metastases. We hypothesized that perfusion parameters will outperform traditional size measurements in discriminating treatment success and failure. MATERIALS AND METHODS: This retrospective study included 49 patients (mean age, 63 [SD, 13] years; 29 men) with metastatic lesions treated with radiation therapy who underwent dynamic contrast-enhanced MR imaging. The median time between radiation therapy and follow-up dynamic contrast-enhanced MR imaging was 62 days. We divided patients into 2 groups: clinical success (n = 38) and failure (n = 11). Failure was defined as PET recurrence (n = 5), biopsy-proved (n = 1) recurrence, or an increase in tumor size (n = 7), while their absence defined clinical success. A Mann-Whitney U test was performed to assess differences between groups. RESULTS: The reduction in plasma volume was greater in the success group than in the failure group (-57.3% versus +88.2%, respectively; P < .001). When we assessed the success of treatment, the sensitivity of plasma volume was 91% (10 of 11; 95% CI, 82%-97%) and the specificity was 87% (33 of 38; 95% CI, 73%-94%). The sensitivity of size measurements was 82% (9 of 11; 95% CI, 67%-90%) and the specificity was 47% (18 of 38; 95% CI, 37%-67%). CONCLUSIONS: The specificity of plasma volume was higher than that of conventional size measurements, suggesting that dynamic contrast-enhanced MR imaging is a powerful tool to discriminate between treatment success and failure.

Delay of Aortic Arterial Input Function Time Improves Detection of Malignant Vertebral Body Lesions on Dynamic Contrast-Enhanced MRI Perfusion.

Dynamic contrast-enhanced MRI (DCE) is an emerging modality in the study of vertebral body malignancies. DCE-MRI analysis relies on a pharmacokinetic model, which assumes that contrast uptake is simultaneous in the feeding of arteries and tissues of interest. While true in the highly vascularized brain, the perfusion of the spine is delayed. This delay of contrast reaching vertebral body lesions can affect DCE-MRI analyses, leading to misdiagnosis for the presence of active malignancy in the bone marrow. To overcome the limitation of delayed contrast arrival to vertebral body lesions, we shifted the arterial input function (AIF) curve over a series of phases and recalculated the plasma volume values (Vp) for each phase shift. We hypothesized that shifting the AIF tracer curve would better reflect actual contrast perfusion, thereby improving the accuracy of Vp maps in metastases. We evaluated 18 biopsy-proven vertebral body metastases in which standard DCE-MRI analysis failed to demonstrate the expected increase in Vp. We manually delayed the AIF curve for multiple phases, defined as the scan-specific phase temporal resolution, and analyzed DCE-MRI parameters with the new AIF curves. All patients were found to require at least one phase-shift delay in the calculated AIF to better visualize metastatic spinal lesions and improve quantitation of Vp. Average normalized Vp values were 1.78 ± 1.88 for zero phase shifts (P0), 4.72 ± 4.31 for one phase shift (P1), and 5.59 ± 4.41 for two phase shifts (P2). Mann-Whitney U tests obtained p-values = 0.003 between P0 and P1, and 0.0004 between P0 and P2. This study demonstrates that image processing analysis for DCE-MRI in patients with spinal metastases requires a careful review of signal intensity curve, as well as a possible adjustment of the phase of aortic AIF to increase the accuracy of Vp.

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome and costly. Purpose To investigate whether clinically generated image annotations can be data mined from the picture archiving and communication system (PACS), automatically curated, and used for semisupervised training of a brain MRI tumor detection model. Materials and Methods In this retrospective study, the cancer center PACS was mined for brain MRI scans acquired between January 2012 and December 2017 and included all annotated axial T1 postcontrast images. Line annotations were converted to boxes, excluding boxes shorter than 1 cm or longer than 7 cm. The resulting boxes were used for supervised training of object detection models using RetinaNet and Mask region-based convolutional neural network (R-CNN) architectures. The best-performing model trained from the mined data set was used to detect unannotated tumors on training images themselves (self-labeling), automatically correcting many of the missing labels. After self-labeling, new models were trained using this expanded data set. Models were scored for precision, recall, and F1 using a held-out test data set comprising 754 manually labeled images from 100 patients (403 intra-axial and 56 extra-axial enhancing tumors). Model F1 scores were compared using bootstrap resampling. Results The PACS query extracted 31 150 line annotations, yielding 11 880 boxes that met inclusion criteria. This mined data set was used to train models, yielding F1 scores of 0.886 for RetinaNet and 0.908 for Mask R-CNN. Self-labeling added 18 562 training boxes, improving model F1 scores to 0.935 (P < .001) and 0.954 (P < .001), respectively. Conclusion The application of semisupervised learning to mined image annotations significantly improved tumor detection performance, achieving an excellent F1 score of 0.954. This development pipeline can be extended for other imaging modalities, repurposing unused data silos to potentially enable automated tumor detection across radiologic modalities. © RSNA, 2022 Online supplemental material is available for this article.

T1-weighted Dynamic Contrast-enhanced MRI to Differentiate Nonneoplastic and Malignant Vertebral Body Lesions in the Spine.

Background Dynamic contrast agent-enhanced (DCE) perfusion MRI may help differentiate between nonneoplastic and malignant lesions in the spine. Purpose To investigate the correlation between fractional plasma volume (Vp), a parameter derived from DCE perfusion MRI, and histopathologic diagnosis for spinal lesions. Materials and Methods In this retrospective study, patients who underwent DCE perfusion MRI and lesion biopsy between May 2015 and May 2018 were included. Inclusion criteria were short time interval (<30 days) between DCE perfusion MRI and biopsy, DCE perfusion MRI performed before biopsy, and DCE perfusion MRI performed at the same spine level as biopsy. Exclusion criteria were prior radiation treatment on vertebrae of interest, poor DCE perfusion MRI quality, nondiagnostic biopsy, and extensive spinal metastasis or prior kyphoplasty. One hundred thirty-four lesions were separated into a nonneoplastic group (n = 51) and a malignant group (n = 83) on the basis of histopathologic analysis. Two investigators manually defined regions of interest in the vertebrae. DCE perfusion MRI parameter Vp was calculated by using the Tofts pharmacokinetic two-compartment model. Vp was quantified, normalized to adjacent normal vertebrae, and compared between the two groups. A Mann-Whitney U test and receiver operating characteristic analysis was performed to verify the difference in Vp between the nonneoplastic and malignant groups. Reproducibility was assessed by calculating the Cohen κ coefficient. Results One hundred patients (mean age, 65 years ± 11 [standard deviation]; 52 men) were evaluated. Vp was lower in nonneoplastic lesions versus malignant lesions (1.6 ± 1.3 vs 4.2 ± 3.0, respectively; P < .001). The sensitivity of Vp was 93% (77 of 83; 95% confidence interval [CI]: 85%, 97%), specificity was 78% (40 of 51; 95% CI: 65%, 89%), and area under the receiver operating characteristic curve was 0.88 (95% CI: 0.82, 0.95). Cohen κ coefficient suggested substantial agreement in both intra- (κ = 0.72) and interreader (κ = 0.70) reproducibility. Conclusion This study indicated that dynamic contrast agent-enhanced perfusion MRI parameter, fractional plasma volume, was able to differentiate between nonneoplastic spinal lesions and malignant lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Haller in this issue.

A 3-Dimensional Mapping Analysis of Regional Nodal Recurrences in Breast Cancer.

PURPOSE: The primary goal was to map the anatomic pattern of isolated nodal recurrences (NR) in the supraclavicular (SCV), axillary, and internal mammary nodes (IMNs) in patients with breast cancer treated with curative-intent surgery with or without radiation therapy (RT). Secondary objectives were to assess clinical and pathologic factors associated with patterns of NR and survival rates. METHODS AND MATERIALS: Patients with NR after treatment at a single cancer center during 1998 to 2013 were identified. Patients with prior distant metastases or NR without correlative imaging were excluded. All NRs were overlaid onto representative axial computed tomographic images. Multivariable analysis was performed to identify clinical and pathologic characteristics associated with NR. Kaplan-Meier curves were generated to assess the rate of relapse by nodal region according to pathologic feature or radiation treatment status. RESULTS: The locations of 243 NRs among 153 eligible patients were mapped. The majority of NR occurred in the axilla (42%; 102/243), followed by the IMN (32.5%; 79/243) and the SCV (25.5%; 62/243). Radiation Therapy Oncology Group (RTOG) or European Society for Radiation therapy and Oncology (ESTRO) clinical target volume encompassed 82% (198/243) of NRs. The majority of out-of-field NRs were located in the lateral and posterior SCV region for both RTOG (67%; 30/45) and ESTRO (89%; 49/55) guidelines. The high-risk patients who received regional RT to the SCV relapsed at a similar rate in the medial, but a higher rate in lateral SCV (P = .009), compared with low-risk patients who received no nodal RT. Lymphovascular invasion most strongly associated with IMN NR (P = .001); grade 3 disease highly associated with both IMN (P = .001) and SCV NR (P = .02). The presence of an IMN NR portended for significantly inferior overall survival (OS), compared with an axillary NR, with a 5-year OS of 59% versus 72%, respectively (P = .03). CONCLUSIONS: In this 3-dimensional image-based analysis of NR patterns in breast cancer patients treated with contemporary therapies, the lateral and posterior SCV represented a distinct site of NR that is not routinely included within current breast cancer contouring atlases. Grade 3 breast cancer and LVI were most commonly associated with the development of NR in the SCV. Modifying the CTV to encompass the lateral and posterior SCV in patients with breast cancer with these features might be justified.

Dynamic contrast-enhanced MRI perfusion for differentiating between melanoma and lung cancer brain metastases.

Brain metastases originating from different primary sites overlap in appearance and are difficult to differentiate with conventional MRI. Dynamic contrast-enhanced (DCE)-MRI can assess tumor microvasculature and has demonstrated utility in characterizing primary brain tumors. Our aim was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (Ktrans ) derived from DCE-MRI in distinguishing between melanoma and nonsmall cell lung cancer (NSCLC) brain metastases. Forty-seven NSCLC and 23 melanoma brain metastases were retrospectively assessed with DCE-MRI. Regions of interest were manually drawn around the metastases to calculate Vpmean and Kmeantrans. The Mann-Whitney U test and receiver operating characteristic analysis (ROC) were performed to compare perfusion parameters between the two groups. The Vpmean of melanoma brain metastases (4.35, standard deviation [SD] = 1.31) was significantly higher (P = 0.03) than Vpmean of NSCLC brain metastases (2.27, SD = 0.96). The Kmeantrans values were higher in melanoma brain metastases, but the difference between the two groups was not significant (P = 0.12). Based on ROC analysis, a cut-off value of 3.02 for Vpmean (area under curve = 0.659 with SD = 0.074) distinguished between melanoma brain metastases and NSCLC brain metastases (P < 0.01) with 72% specificity. Our data show the DCE-MRI parameter Vpmean can differentiate between melanoma and NSCLC brain metastases. The ability to noninvasively predict tumor histology of brain metastases in patients with multiple malignancies can have important clinical implications.

Intracranial meningioma with vertebral or intraspinal metastasis: report of 2 cases and review of the literature.

Extracranial meningioma metastases (EMM) occur in 0.1% of intracranial meningioma patients and are more commonly seen in those with atypical and anaplastic histologies. While the lungs and pleura are the most common site of EMM, intraspinal and vertebral EMM also occur and are not well described in the literature. Although the presence of EMM can worsen prognosis, no standard of care has been established for EMM management. All patients treated for recurrent atypical/anaplastic meningiomas between January 1985 and July 2014 at Memorial Sloan Kettering Cancer Center were screened for intraspinal and vertebral EMM. Of these patients, 2 were identified as having recurrent meningioma complicated by vertebral or intraspinal EMM. A review of the literature was also conducted. The PubMed database was screened for intraspinal and vertebral EMM cases reported in the literature from 1985 to 2015. Nineteen articles were identified from the literature and included 24 individual cases with a total of 34 vertebral or intraspinal EMM. Forty-two percent (10/24) of patients with vertebral or intraspinal EMM had WHO Grade I tumors. Furthermore, 25% (6/24) of vertebral and intraspinal EMM occurred after the primary tumor but prior to any recurrence. This paper highlights that vertebral and intraspinal EMM can occur in patients with WHO Grade I meningiomas and can occur before tumor recurrence. This challenges the notion that EMM are seen primarily in high-grade atypical and anaplastic meningiomas.

A novel magnetic resonance imaging segmentation technique for determining diffuse intrinsic pontine glioma tumor volume.

OBJECTIVE Accurately determining diffuse intrinsic pontine glioma (DIPG) tumor volume is clinically important. The aims of the current study were to 1) measure DIPG volumes using methods that require different degrees of subjective judgment; and 2) evaluate interobserver agreement of measurements made using these methods. METHODS Eight patients from a Phase I clinical trial testing convection-enhanced delivery (CED) of a therapeutic antibody were included in the study. Pre-CED, post-radiation therapy axial T2-weighted images were analyzed using 2 methods requiring high degrees of subjective judgment (picture archiving and communication system [PACS] polygon and Volume Viewer auto-contour methods) and 1 method requiring a low degree of subjective judgment (k-means clustering segmentation) to determine tumor volumes. Lin's concordance correlation coefficients (CCCs) were calculated to assess interobserver agreement. RESULTS The CCCs of measurements made by 2 observers with the PACS polygon and the Volume Viewer auto-contour methods were 0.9465 (lower 1-sided 95% confidence limit 0.8472) and 0.7514 (lower 1-sided 95% confidence limit 0.3143), respectively. Both were considered poor agreement. The CCC of measurements made using k-means clustering segmentation was 0.9938 (lower 1-sided 95% confidence limit 0.9772), which was considered substantial strength of agreement. CONCLUSIONS The poor interobserver agreement of PACS polygon and Volume Viewer auto-contour methods highlighted the difficulty in consistently measuring DIPG tumor volumes using methods requiring high degrees of subjective judgment. k-means clustering segmentation, which requires a low degree of subjective judgment, showed better interobserver agreement and produced tumor volumes with delineated borders.

Conventional and Advanced Imaging of Diffuse Intrinsic Pontine Glioma.

Diffuse intrinsic pontine glioma is the most common brainstem tumor in pediatric patients. This tumor remains one of the most deadly pediatric brain tumors. The diagnosis primarily relies on clinical symptoms and imaging findings. Conventional MRI provides a noninvasive accurate method of diagnosis of these tumors. Advanced MRI techniques are becoming more widely used and studied as additional noninvasive methods to assist clinicians in initial diagnosis and staging, monitoring disease, as well as in surgical and radiation planning. This article will provide an overview of DIPG and describe the typical imaging findings with a focus on advanced imaging techniques.

Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma.

BACKGROUND: The majority of WHO grades II and III gliomas harbor a missense mutation in the metabolic gene isocitrate dehydrogenase (IDH) and accumulate the metabolite R-2-hydroxyglutarate (R-2HG). Prior studies showed that this metabolite can be detected in vivo using proton magnetic-resonance spectroscopy (MRS), but the sensitivity of this methodology and its clinical implications are unknown. METHODS: We developed an MR imaging protocol to integrate 2HG-MRS into routine clinical glioma imaging and examined its performance in 89 consecutive glioma patients. RESULTS: Detection of 2-hydroxyglutarate (2HG) in IDH-mutant gliomas was closely linked to tumor volume, with sensitivity ranging from 8% for small tumors (<3.4 mL) to 91% for larger tumors (>8 mL). In patients undergoing 2HG-MRS prior to surgery, tumor levels of 2HG corresponded with tumor cellularity but not with tumor grade or mitotic index. Cytoreductive therapy resulted in a gradual decrease in 2HG levels with kinetics that closely mirrored changes in tumor volume. CONCLUSIONS: Our study demonstrates that 2HG-MRS can be linked with routine MR imaging to provide quantitative measurements of 2HG in glioma and may be useful as an imaging biomarker to monitor the abundance of IDH-mutant tumor cells noninvasively during glioma therapy and disease monitoring.