Mixed histiocytic neoplasms: A multicentre series revealing diverse somatic mutations and responses to targeted therapy.

Histiocytic neoplasms are diverse clonal haematopoietic disorders, and clinical disease is mediated by tumorous infiltration as well as uncontrolled systemic inflammation. Individual subtypes include Langerhans cell histiocytosis (LCH), Rosai-Dorfman-Destombes disease (RDD) and Erdheim-Chester disease (ECD), and these have been characterized with respect to clinical phenotypes, driver mutations and treatment paradigms. Less is known about patients with mixed histiocytic neoplasms (MXH), that is two or more coexisting disorders. This international collaboration examined patients with biopsy-proven MXH with respect to component disease subtypes, oncogenic driver mutations and responses to conventional (chemotherapeutic or immunosuppressive) versus targeted (BRAF or MEK inhibitor) therapies. Twenty-seven patients were studied with ECD/LCH (19/27), ECD/RDD (6/27), RDD/LCH (1/27) and ECD/RDD/LCH (1/27). Mutations previously undescribed in MXH were identified, including KRAS, MAP2K2, MAPK3, non-V600-BRAF, RAF1 and a BICD2-BRAF fusion. A repeated-measure generalized estimating equation demonstrated that targeted treatment was statistically significantly (1) more likely to result in a complete response (CR), partial response (PR) or stable disease (SD) (odds ratio [OR]: 17.34, 95% CI: 2.19-137.00, p = 0.007), and (2) less likely to result in progression (OR: 0.08, 95% CI: 0.03-0.23, p < 0.0001). Histiocytic neoplasms represent an entity with underappreciated clinical and molecular diversity, poor responsiveness to conventional therapy and exquisite sensitivity to targeted therapy.

Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI.

OBJECTIVES: While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data. METHODS: In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refinement cycle, a new model was trained and tested on a held-out set of 319 manually segmented image slices (93 adult patients), with the SSL cycles continuing until Dice score coefficient (DSC) peaked. DSCs were compared using bootstrap resampling. Utilizing the best-performing models, two inference methods were compared: (1) conventional full-image segmentation, and (2) a hybrid method augmenting full-image segmentation with detection plus image patch segmentation. RESULTS: Baseline segmentation models achieved DSC of 0.768 (U-Net), 0.831 (Mask R-CNN), and 0.838 (HRNet), improving with self-refinement to 0.798, 0.871, and 0.873 (each p < 0.001), respectively. Hybrid inference outperformed full image segmentation alone: DSC 0.884 (Mask R-CNN) vs. 0.873 (HRNet), p < 0.001. CONCLUSIONS: Line annotations mined from PACS can be harnessed within an automated pipeline to produce accurate brain MRI tumor segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities. KEY POINTS: • A brain MRI tumor detection model trained using clinical line measurement annotations mined from PACS was leveraged to automatically generate tumor segmentation pseudo-masks. • An iterative self-refinement process automatically improved pseudo-mask quality, with the best-performing segmentation pipeline achieving a Dice score of 0.884 on a held-out test set. • Tumor line measurement annotations generated in routine clinical radiology practice can be harnessed to develop high-performing segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

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.

Phase 1b trial of an ibrutinib-based combination therapy in recurrent/refractory CNS lymphoma.

Ibrutinib is a first-in-class inhibitor of Bruton tyrosine kinase (BTK) and has shown single-agent activity in recurrent/refractory central nervous system (CNS) lymphoma. Clinical responses are often transient or incomplete, suggesting a need for a combination therapy approach. We conducted a phase 1b clinical trial to explore the sequential combination of ibrutinib (560 or 840 mg daily dosing) with high-dose methotrexate (HD-MTX) and rituximab in patients with CNS lymphoma (CNSL). HD-MTX was given at 3.5 g/m2 every 2 weeks for a total of 8 doses (4 cycles; 1 cycle = 28 days). Ibrutinib was held on days of HD-MTX infusion and resumed 5 days after HD-MTX infusion or after HD-MTX clearance. Single-agent daily ibrutinib was administered continuously after completion of induction therapy until disease progression, intolerable toxicity, or death. We also explored next-generation sequencing of circulating tumor DNA (ctDNA) in cerebrospinal fluid (CSF) before and during treatment. The combination of ibrutinib, HD-MTX, and rituximab was tolerated with an acceptable safety profile (no grade 5 events, 3 grade 4 events). No dose-limiting toxicity was observed. Eleven of 15 patients proceeded to maintenance ibrutinib after completing 4 cycles of the ibrutinib/HD-MTX/rituximab combination. Clinical responses occurred in 12 of 15 patients (80%). Sustained tumor responses were associated with clearance of ctDNA from the CSF. This trial was registered at www.clinicaltrials.gov as #NCT02315326.

Prognostic value of [18F]FDG PET/CT in patients with CNS lymphoma receiving ibrutinib-based therapies.

PURPOSE: Current clinical and imaging tools remain suboptimal for predicting treatment response and prognosis in CNS lymphomas. We investigated the prognostic value of baseline [18F]FDG PET in patients with CNS lymphoma receiving ibrutinib-based treatments. METHODS: Fifty-three patients enrolled in a prospective clinical trial and underwent brain PET before receiving single-agent ibrutinib or ibrutinib in combination with methotrexate with or without rituximab. [18F]FDG uptake in these lesions was quantified by drawing PET volumes of interest around up to five [18F]FDG-avid lesions per patient (with uptake greater than surrounding brain). We measured standardized uptake values (SUVmax), metabolic tumor volumes, total lesion glycolysis (TLG), and the sum thereof in these lesions. We analyzed the relationship between PET parameters and mutation status, overall response rates, and progression-free survival (PFS). RESULTS: Thirty-eight patients underwent single-agent therapy and 15 received combination therapy. On PET, 15/53 patients had no measurable disease. In the other 38 patients, a total of 71 lesions were identified on PET. High-intensity [18F]FDG uptake and a larger volume of [18F]FDG-avid disease were inversely related to treatment outcome (p ≤ 0.005). In univariable analysis, PFS was linearly correlated with all PET parameters, with stronger association when sum-values were used. A multivariable model showed that risk of progression increased by 9% for every 5-unit increase in sumSUVmax (hazard ratio = 1.09 [95% CI: 1.04 to 1.14]). CONCLUSION: Higher lesional metabolic parameters are inversely related to outcome in patients undergoing ibrutinib-based therapies, and sumSUVmax emerged as a strong independent prognostic factor. TRIAL REGISTRATION: NCT02315326; https://clinicaltrials.gov/ct2/show/NCT02315326?term=NCT02315326&draw=2&rank=1.

18F-FDG PET/CT versus anatomic imaging for evaluating disease extent and clinical trial eligibility in Erdheim-Chester disease: results from 50 patients in a registry study.

OBJECTIVES: The aim of this study was to [1] characterize distribution of Erdheim-Chester Disease (ECD) by 18F-FDG PET/CT and [2] determine the utility of metabolic (18F-FDG PET/CT) imaging versus anatomic imaging (CT or MRI) in evaluating ECD patients for clinical trial eligibility. METHODS: 18F-FDG PET/CT and corresponding CT or MRI studies for ECD patients enrolled in a prospective registry study were reviewed. Sites of disease were classified as [1] detectable by 18F-FDG PET only, CT/MRI only, or both and as [2] measurable by modified PERCIST (mPERCIST) only, RECIST only, or both. Descriptive analysis was performed and paired t test for between-group comparisons. RESULTS: Fifty patients were included (mean age 51.5 years; range 18-70 years). Three hundred thirty-three disease sites were detected among all imaging modalities, 188 (56%) by both 18F-FDG PET and CT/MRI, 67 (20%) by 18F-FDG PET only, 75 (23%) by MRI brain only, and 3 (1%) by CT only. Of 178 disease sites measurable by mPERCIST or RECIST, 40 (22%) were measurable by both criteria, 136 (76%) by mPERCIST only, and 2 (1%) by RECIST only. On the patient level, 17 (34%) had mPERCIST and RECIST measurable disease, 30 (60%) had mPERCIST measurable disease only, and 0 had RECIST measurable disease only (p < 0.0001). CONCLUSION: Compared with anatomic imaging, 18F-FDG PET/CT augments evaluation of disease extent in ECD and increases identification of disease sites measurable by formal response criteria and therefore eligibility for clinical trials. Complementary organ-specific anatomic imaging offers the capacity to characterize sites of disease in greater anatomic detail. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03329274.

Identification of HER2-Positive Metastases in Patients with HER2-Negative Primary Breast Cancer by Using HER2-targeted 89Zr-Pertuzumab PET/CT.

Background Human epidermal growth factor receptor 2 (HER2)-targeted therapies are successful in patients with HER2-positive malignancies; however, spatial and temporal heterogeneity of HER2 expression may prevent identification of optimal patients for these therapies. Purpose To determine whether imaging with the HER2-targeted PET tracer zirconium 89 (89Zr)-pertuzumab can depict HER2-positive metastases in women with HER2-negative primary breast cancer. Materials and Methods From January to June 2019, women with biopsy-proven HER2-negative primary breast cancer and biopsy-proven metastatic disease were enrolled in a prospective clinical trial (ClinicalTrials.gov NCT02286843) and underwent 89Zr-pertuzumab PET/CT for noninvasive whole-biopsy evaluation of potential HER2-positive metastases. 89Zr-pertuzumab-avid foci that were suspicious for HER2-positive metastases were tissue sampled and examined by pathologic analysis to document HER2 status. Results Twenty-four women (mean age, 55 years ± 11 [standard deviation]) with HER2-negative primary breast cancer were enrolled. Six women demonstrated foci at 89Zr-pertuzumab PET/CT that were suspicious for HER2-positive disease. Of these six women, three had biopsy-proven HER2-positive metastases, two had pathologic findings that demonstrated HER2-negative disease, and one had a fine-needle aspirate with inconclusive results. Conclusion Human epidermal growth factor receptor 2 (HER2)-targeted imaging with zirconium 89-pertuzumab PET/CT was successful in detecting HER2-positive metastases in women with HER2-negative primary breast cancer. This demonstrates the ability of targeted imaging to identify patients for targeted therapies that might not otherwise be considered. © RSNA, 2020 Online supplemental material is available for this article. See the editorial by Mankoff and Pantel in this issue.

Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.

The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.

Optimal mass transport kinetic modeling for head and neck DCE-MRI: Initial analysis.

PURPOSE: Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows. METHOD: Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and underwent DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ( ΦF ) and backward flux ( ΦB ). OMT-derived ΦF was compared with the volume transfer constant for CA, Ktrans , derived from the Extended Tofts Model (ETM). RESULTS: The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 ± 0.0091 ( min-1 ) whereas the mean advective component was 0.0052 ± 0.0086 ( min-1 ) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67% to 18.76% and 12.76% to 30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ( ΦF ) is comparable with the ETM-derived Ktrans . CONCLUSIONS: This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.

Pretreatment dynamic contrast-enhanced MRI biomarkers correlate with progression-free survival in primary central nervous system lymphoma.

PURPOSE: Prediction of clinical outcomes in patients with primary central nervous system lymphoma (PCNSL) is important for optimization of treatment planning. Quantitative imaging biomarkers for PCNSL have not yet been established. This study evaluated the prognostic value of pretreatment dynamic contrast-enhanced MRI and diffusion-weighted imaging for progression-free survival (PFS) in patients with PCNSL. METHODS: Pretreatment dynamic contrast-enhanced MRI and diffusion-weighted imaging were retrospectively analyzed in 18 immunocompetent patients with PCNSL. Volumes of interest encompassing the tumors were assessed for measurements of blood plasma volume (Vp), volume transfer constant (Ktrans), and apparent diffusion coefficient. Patients were divided into short and long PFS groups based on median PFS. Imaging and clinical variables were correlated with PFS. RESULTS: Median PFS was 19.6 months. Lower Vpmean and Ktransmean values increased risk for rapid progression (< 19.6 months). Receiver operating characteristic curve analysis demonstrated an optimal Vpmean cutoff value of 2.29 (area under the curve [AUC] = 0.74, sensitivity and specificity = 0.78, p = 0.023) for separating patients with short and long PFS. The optimal Ktransmean cutoff was 0.08 (AUC = 0.74, sensitivity = 0.67, specificity = 0.78, p = 0.025). Kaplan-Meier survival analysis with log-rank test demonstrated significantly (p = 0.015) increased risk of rapid progression for patients with Vpmean < 2.29. Vpmean was significantly (p = 0.03) associated with PFS on univariate Cox analysis. Apparent diffusion coefficient values and clinical factors did not influence PFS. CONCLUSIONS: Pretreatment Vp and Ktrans derived from dynamic contrast-enhanced MRI may be novel prognostic quantitative imaging biomarkers of progression-free survival in patients with PCNSL. These data should be prospectively validated in larger patient cohorts.

Intravoxel incoherent motion diffusion-weighted MRI during chemoradiation therapy to characterize and monitor treatment response in human papillomavirus head and neck squamous cell carcinoma.

PURPOSE: Characterize and monitor treatment response in human papillomavirus (HPV) head and neck squamous cell carcinoma (HNSCC) using intra-treatment (intra-TX) imaging metrics derived from intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (DW-MRI). MATERIALS AND METHODS: Thirty-four (30 HPV positive [+] and 4 HPV negative [-]) HNSCC patients underwent a total of 136 MRI including multi-b value DW-MRI (pretreatment [pre-TX] and intra-TX weeks 1, 2, and 3) at 3.0 Tesla. All patients were treated with chemo-radiation therapy. Monoexponential (yielding apparent diffusion coefficient [ADC]) and bi-exponential (yielding perfusion fraction [f], diffusion [D], and pseudo-diffusion [D*] coefficients) fits were performed on a region of interest and voxel-by-voxel basis, on metastatic neck nodes. Response was assessed using RECISTv1.1. The relative percentage change in D, f, and D* between the pre- and intra-TX weeks were used for hierarchical clustering. A Wilcoxon rank-sum test was performed to assess the difference in metrics within and between the complete response (CR) and non-CR groups. RESULTS: The delta (Δ) change in volume (V)1wk-0wk for the CR group differed significantly (P = 0.016) from the non-CR group, while not for V2wk-0wk and V3wk-0wk (P > 0.05). The mean increase in ΔD3wk-0wk for the CR group was significantly higher (P = 0.017) than the non-CR group. ADC and D showed an increasing trend at each intra-TX week when compared with pre-TX in CR group (P < 0.003). Hierarchical clustering demonstrated the existence of clusters in HPV + patients. CONCLUSION: After appropriate validation in a larger population, these IVIM imaging metrics may be useful for individualized treatment in HNSCC patients. LEVEL OF EVIDENCE: 2 J. Magn. Reson. Imaging 2017;45:1013-1023.

Second-opinion interpretations of neuroimaging studies by oncologic neuroradiologists can help reduce errors in cancer care.

BACKGROUND: The purpose of this study was to investigate the utility and clinical impact of second-opinion interpretations of outside neuroimaging studies by oncologic neuroradiologists at a National Cancer Institute-designated cancer center. METHODS: We performed a retrospective analysis of initial outside and second-opinion radiology reports from 300 computed tomography and magnetic resonance imaging studies and identified cases with discrepancies between the two reports. An adult neuro-oncologist, pediatric neuro-oncologist, and head and neck surgeon reviewed each pair of discrepant reports based on their area of expertise, patient age, and the type of study performed. The clinicians were blinded to the origin of each report and recorded whether the differences in the reports would have led to a change in patient management and/or disease staging. Histopathologic analysis, clinical assessment, and/or minimum 3-month imaging follow-up served as the reference standards to establish which of the 2 reports was correct. RESULTS: Among the 283 cases that met our study criteria, there were 55 neuroimaging studies with disagreements (19%) between the initial outside report and second-opinion interpretation. Patient management and/or disease stage would have been altered in 42 of 283 cases (15%) based on report differences as determined by the 2 neuro-oncologists and the surgeon participating in the study. Sufficient follow-up was available in 35 of 42 cases (83%). The second-opinion interpretation was correct 100% of the time (35/35). CONCLUSION: Second-opinion interpretations of neuroimaging studies by subspecialized oncologic neuroradiologists provide added value by reducing error and optimizing the care of cancer patients. Cancer 2016. © 2016 American Cancer Society. Cancer 2016;122:2708-2714. © 2016 American Cancer Society.

Nonalcoholic Thiamine-Related Encephalopathy (Wernicke-Korsakoff Syndrome) Among Inpatients With Cancer: A Series of 18 Cases.

BACKGROUND: Wernicke-Korsakoff Syndrome (WKS) is a neuropsychiatric syndrome caused by thiamine deficiency. Cancer predisposes to thiamine deficiency through various mechanisms. Although many case reports exist on nonalcoholic WKS in cancer, larger qualitative studies are lacking. METHOD: Retrospective study of patients admitted to a cancer hospital and diagnosed with WKS during routine care on a psychiatric consultation service. Only patients with at least 1 additional supporting feature (magnetic resonance imaging findings, low serum thiamine concentrations, or response to treatment) were included. Data pertaining to demographics, risk factors, phenomenology, and outcomes were abstracted from medical records by chart review. RESULTS: In all, 18 patients were included. All patients developed WKS during cancer treatment. Hematologic malignancy, gastrointestinal tract tumors, low oral intake, and weight loss were common risk factors. All patients presented with cognitive dysfunction, most commonly impaired alertness, attention, and short-term memory. All were diagnosed by operational criteria proposed by Caine et al., 1997 (where 2 of the following are required: nutritional deficiency, ocular signs, cerebellar signs, and either altered mental status or mild memory impairment). Few exhibited Wernicke's classic triad. Diagnostic and treatment delay were common. Only 3 patients recovered fully. CONCLUSION: Nonalcoholic WKS can occur during cancer treatment and manifests clinically as delirium. Diagnosis should be made using operational criteria, not Wernicke's triad. Most patients were not underweight and had normal serum concentration of vitamin B12 and folate. A variety of mechanisms might predispose to thiamine deficiency and WKS in cancer. Given the high frequency of residual morbidity, studies should focus on decreasing diagnostic and treatment delay.

Repeatability Investigation of Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging on Thyroid Glands.

OBJECTIVE: To investigate the repeatability of the quantitative magnetic resonance imaging (MRI) metric (apparent diffusion coefficient [ADC]) derived from reduced field-of-view diffusion-weighted (rFOV DWI) on thyroid glands in a clinical setting. MATERIALS AND METHODS: Ten healthy human volunteers were enrolled in MRI studies performed on a 3-T MRI scanner. Each volunteer was designed to undergo 3 longitudinal examinations (2 weeks apart) with 2 repetitive sessions within each examination, which included rFOV and conventional full field-of-view (fFOV) DWI scans. Diffusion-weighted images were assessed and scored based on image characteristics. Apparent diffusion coefficient values of thyroid glands from all participants were calculated based on regions of interest. Repeatability analysis was performed based on the framework proposed by the Quantitative Imaging Biomarker Alliance, generating 4 repeatability metrics: within-participant variance ((Equation is included in full-text article.)), repeatability coefficients, intraclass correlation coefficient, and within-participant coefficient of variation. Student t test was used to compare the performance difference between rFOV and fFOV DWI. RESULTS: The overall image quality from rFOV DWI was significantly higher than that from fFOV DWI (P = 0.04). The ADC values calculated from rFOV DWI were significantly lower than corresponding values from fFOV DWI (P < 0.001). There was no significant difference in ADC values across sessions and examinations in either rFOV or fFOV DWI (P > 0.05). Reduced field-of-view DWI had lower values of (Equation is included in full-text article.), repeatability coefficient, and within-participant coefficient of variation and had a higher value of intraclass correlation coefficient compared with fFOV DWI across either sessions or examinations. CONCLUSIONS: This study demonstrated that rFOV DWI produced more superior-quality DWI images and more repeatable ADC measurements compared with fFOV DWI, thus providing a feasible quantitative imaging tool for investigating thyroid glands in clinical settings.

Palliative treatment of thiamine-related encephalopathy (Wernicke's encephalopathy) in cancer: A case series and review of the literature.

OBJECTIVE: Thiamine-related encephalopathy (Wernicke's encephalopathy) is a neuropsychiatric syndrome caused by a vitamin B1 (thiamine) deficiency often associated with alcoholism. Cancer predisposes patients to thiamine deficiency unrelated to alcoholism, though many cases are missed clinically. The present report adds to the literature on thiamine as a palliative tool for thiamine-related encephalopathy (TRE) in cancer. METHOD: From a larger series of TRE in cancer, we report on three cases with terminal illness. RESULTS: Case 1. A 61-year old woman with Hodgkin's lymphoma developed TRE over 13 days. Precipitants included a hypermetabolic state in the background of subacute thiamine deficiency. Diagnosis was supported by abnormal serum thiamine and positive MRI findings. Mental status improved within 36 hours of initiating thiamine 500 mg IV t.i.d. Case 2. A 68-year-old man with colon cancer metastatic to liver and bone developed TRE precipitated by C. difficile-related diarrhea superimposed on 3 months of low appetite and weight loss. Diagnosis was supported by abnormal serum thiamine, and thiamine 500 mg IV t.i.d. was initiated. Improvements in mental status began within 36 hours. Case 3. An 80-year-old man with squamous cell carcinoma developed TRE precipitated by systemic infection in the context of three weeks of dysphagia. Antibiotic treatment did not reverse his cognitive symptoms, and a diagnosis of TRE was made based on operationalized criteria. Thiamine 100 mg IV daily did not reverse his symptoms. On his 30th day of admission, thiamine was increased to 500 mg IV t.i.d., resulting in a rapid reversal of altered mental status. SIGNIFICANCE OF RESULTS: This report adds to the list of cancer types in which TRE/Wernicke's encephalopathy has been reported. It supports the use of higher doses of thiamine than are typically recommended in North America. Improvement following treatment allowed patients to engage with family and treatment teams prior to death.

Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.

Objectives: Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T2-weighted (T2w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods: This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T2w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients (ρ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P-values <0.05 were considered significant. Results: No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm3, P = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm3, with a mean difference of 0.30 cm3. SMIT model and manually delineated tumor volume estimates were highly correlated (ρ = 0.84-0.96, P < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively. Conclusions: The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC. Advances in knowledge: First evaluation of auto-segmentation with SMIT using longitudinal T2w MRI in HPV+ OPSCC.

Differentiating Low-Grade from High-Grade Intracranial Ependymomas: Comparison of Dynamic Contrast-Enhanced MRI and Diffusion-Weighted Imaging.

BACKGROUND AND PURPOSE: The aim of this study was to determine the diagnostic value of fractional plasma volume derived from dynamic contrast-enhanced perfusion MR imaging versus ADC, obtained from DWI in differentiating between grade 2 (low-grade) and grade 3 (high-grade) intracranial ependymomas. MATERIALS AND METHODS: A hospital database was created for the period from January 2013 through June 2022, including patients with histologically-proved ependymoma diagnosis with available dynamic contrast-enhanced MR imaging. Both dynamic contrast-enhanced perfusion and DWI were performed on each patient using 1.5T and 3T scanners. Fractional plasma volume maps and ADC maps were calculated. ROIs were defined by a senior neuroradiologist manually by including the enhancing tumor on every section and conforming a VOI to obtain the maximum value of fractional plasma volume (Vpmax) and the minimum value of ADC (ADCmin). A Mann-Whitney U test at a significance level of corrected P = .01 was used to evaluate the differences. Additionally, receiver operating characteristic curve analysis was applied to assess the sensitivity and specificity of Vpmax and ADCmin values. RESULTS: A total of 20 patients with ependymomas (10 grade 2 tumors and 10 grade 3 tumors) were included. Vpmax values for grade 3 ependymomas were significantly higher (P < .002) than those for grade 2. ADCmin values were overall lower in high-grade lesions. However, no statistically significant differences were found (P = .12114). CONCLUSIONS: As a dynamic contrast-enhanced perfusion MR imaging metric, fractional plasma volume can be used as an indicator to differentiate grade 2 and grade 3 ependymomas. Dynamic contrast-enhanced perfusion MR imaging plays an important role with high diagnostic value in differentiating low- and high-grade ependymoma.

Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy.

Background and purpose: Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma. Materials and methods: Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT. Results: Significant correlations with volume loss were observed for baseline FDG SUVmean (Spearman ρ = 0.46, p < 0.001), CT HUmean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, Dmean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUVmean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUVmean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors. Conclusions: Multiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis.

Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization.

Reliable and trustworthy artificial intelligence (AI), particularly in high-stake medical diagnoses, necessitates effective uncertainty quantification (UQ). Existing UQ methods using model ensembles often introduce invalid variability or computational complexity, rendering them impractical and ineffective in clinical workflow. We propose a UQ approach based on deep neuroevolution (DNE), a data-efficient optimization strategy. Our goal is to replicate trends observed in expert-based UQ. We focused on language lateralization maps from resting-state functional MRI (rs-fMRI). Fifty rs-fMRI maps were divided into training/testing (30:20) sets, representing two labels: "left-dominant" and "co-dominant." DNE facilitated acquiring an ensemble of 100 models with high training and testing set accuracy. Model uncertainty was derived from distribution entropies over the 100 model predictions. Expert reviewers provided user-based uncertainties for comparison. Model (epistemic) and user-based (aleatoric) uncertainties were consistent in the independently and identically distributed (IID) testing set, mainly indicating low uncertainty. In a mostly out-of-distribution (OOD) holdout set, both model and user-based entropies correlated but displayed a bimodal distribution, with one peak representing low and another high uncertainty. We also found a statistically significant positive correlation between epistemic and aleatoric uncertainties. DNE-based UQ effectively mirrored user-based uncertainties, particularly highlighting increased uncertainty in OOD images. We conclude that DNE-based UQ correlates with expert assessments, making it reliable for our use case and potentially for other radiology applications.

A Phase II study assessing Long-Term Response to Ibrutinib Monotherapy in recurrent or refractory CNS Lymphoma.

BACKGROUND: Ibrutinib is a first-in-class inhibitor of Bruton tyrosine kinase (BTK). We previously reported the safety and short-term antitumor activity of ibrutinib in 20 patients with relapsed or refractory (r/r) primary (PCNSL) or secondary CNS lymphoma (SCNSL). PATIENTS AND METHODS: We enrolled 26 additional patients with r/r PCNSL/SCNSL into the dose-expansion cohort of the trial into a combined cohort of 46 patients (31 PCNSLs, 15 SCNSLs). Patients received ibrutinib at 560mg or 840mg daily in the dose-escalation and 840mg daily in the expansion cohort. Median follow up was 49.9 and 62.1 months for patients with PCNSL and SCNSL, respectively. We sequenced DNA from available tumor biopsies and cerebrospinal fluid (CSF) collected before and during ibrutinib therapy. RESULTS: Tumor responses were observed in 23/31 (74%) PCNSLs and 9/15 (60%) SCNSLs, including 12 complete responses in PCNSL and 7 in SCNSL. Median progression-free survival (PFS) for PCNSL was 4.5 months (95%CI: 2.8-9.2) with 1y-PFS at 23.7% (95%CI: 12.4%-45.1%). Median duration of response (DOR) in the 23 PCNSL responders was 5.5 months. Median PFS in SCNSL was 5.3 months (95%CI: 1.3-14.5) with a median DOR 8.7 months for the 9 responders. Exploratory biomarker analysis suggests that mutations in TBL1XR1 may be associated with a long-term response to ibrutinib in PCNSL (p=0.0075). Clearance of circulating tumor DNA from CSF was associated with complete and long-term ibrutinib response. CONCLUSIONS: Our study confirms single-agent activity of ibrutinib in r/r CNS lymphoma and identifies molecular determinants of response based on long-term follow up. CLINICAL TRIAL INFORMATION: NCT02315326.