Longitudinal MR spectroscopy to detect progression in patients with lower-grade glioma in the surveillance phase.

Background: Monitoring lower-grade gliomas (LrGGs) for disease progression is made difficult by the limits of anatomical MRI to distinguish treatment related tissue changes from tumor progression. MR spectroscopic imaging (MRSI) offers additional metabolic information that can help address these challenges. The goal of this study was to compare longitudinal changes in multiparametric MRI, including diffusion weighted imaging, perfusion imaging, and 3D MRSI, for LrGG patients who progressed at the final time-point and those who remained clinically stable. Methods: Forty-one patients with LrGG who were clinically stable were longitudinally assessed for progression. Changes in anatomical, diffusion, perfusion and MRSI data were acquired and compared between patients who remained clinically stable and those who progressed. Results: Thirty-one patients remained stable, and 10 patients progressed. Over the study period, progressed patients had a significantly greater increase in normalized choline, choline-to-N-acetylaspartic acid index (CNI), normalized creatine, and creatine-to-N-acetylaspartic acid index (CRNI), than stable patients. CRNI was significantly associated with progression status and WHO type. Progressed astrocytoma patients had greater increases in CRNI than stable astrocytoma patients. Conclusions: LrGG patients in surveillance with tumors that progressed had significantly increasing choline and creatine metabolite signals on MRSI, with a trend of increasing T2 FLAIR volumes, compared to LrGG patients who remained stable. These data show that MRSI can be used in conjunction with anatomical imaging studies to gain a clearer picture of LrGG progression, especially in the setting of clinical ambiguity.

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm.

PURPOSE: To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss. MATERIALS AND METHODS: In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients. RESULTS: The U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P < .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98). CONCLUSION: For brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.Keywords: Neural Networks, Brain/Brain Stem, Segmentation Supplemental material is available for this article. © RSNA, 2021.

Neuro-Oncology Practice Clinical Debate: FDG PET to differentiate glioblastoma recurrence from treatment-related changes.

The ability to accurately differentiate treatment-related changes (ie, pseudoprogression and radiation necrosis) from recurrent glioma remains a critical diagnostic problem in neuro-oncology. Because these entities are treated differently and have vastly different outcomes, accurate diagnosis is necessary to provide optimal patient care. In current practice, this diagnostic quandary commonly requires either serial imaging or histopathologic tissue confirmation. In this article, experts in the field debate the utility of 2-deoxy-2[18F]fluoro-d-glucose positron emission tomography (FDG PET) as an imaging tool to distinguish tumor recurrence from treatment-related changes in a patient with glioblastoma and progressive contrast enhancement on magnetic resonance (MR) following chemoradiotherapy.

Imaging, Histopathologic, and Treatment Nuances of Pulmonary Carcinosarcoma.

A 76-year-old female with coronary artery disease, chronic obstructive pulmonary disease, diabetes mellitus type II, and 40 pack-year smoking history presented with a four-day history of cough, productive of green-yellow sputum. Chest X-ray revealed opacification of the left upper lung field, and computed tomography (CT) of the chest showed a large cavitary lesion invading the T2-T3 vertebral bodies, extending into the epidural space, giving rise to mild cord compression. Biopsy of the lesion revealed a poorly differentiated neoplasm composed of distinct epithelial and mesenchymal components, consistent with carcinosarcoma. A metastatic workup was negative. Primary lung carcinosarcoma is a rare tumour that can demonstrate an especially aggressive clinical course; diagnosis is often nuanced by limited sampling at initial presentation, especially in a setting of advanced disease and debility that precludes consideration for upfront resection or more extensive, invasive sampling.

It's not what it looks like: atypical rash in cryoglobulinaemic vasculitis.

A 48-year-old man with a history of intravenous drug use and chronic, untreated hepatitis C presented to the emergency room with acute bilateral lower extremity swelling, erythema and maculopapular rash. Serum C4 levels were low, but dermatology felt the rash was due to venous stasis dermatitis. The patient was discharged with compression stockings, but returned to the hospital 5 days later with no improvement in his symptoms. A more extensive laboratory workup revealed hepatitis C viral load of 4 million, elevated serum cryoglobulins, and skin biopsy showing leucocytoclastic vasculitis. He was treated with oral prednisone, with complete resolution of his symptoms after 2 weeks. He was scheduled for follow-up in gastroenterology clinic for treatment of his hepatitis C for definitive cure of his mixed cryoglobulinaemia, but failed to get insurance authorisation to begin treatment with Harvoni. He presented to the hospital 4 months later with diffuse alveolar haemorrhage.

Obturator Nerve Schwannoma as a Mimic of Ovarian Malignancy.

The obturator nerve is an extremely rare location for schwannomas to originate, and such diagnosis is typically not considered among the imaging diagnostic possibilities for a cystic-solid pelvic mass. A 63-year-old female with a known pelvic mass presented with increasing pelvic pain. The mass, which had been followed by serial imaging over five years, was described showing mixed solid and cystic components, likely arising from the left ovary. Although the key diagnosis to be excluded was a primary ovarian malignancy, the patient chose to pursue active surveillance. Over the five years of close observation, the lesion increased slowly, while her CA-125 level showed no significant elevation. Increase in size of the mass and worsening pain and concern for a gynecologic malignancy on MRI led her to ultimately consent to a hysterectomy with bilateral salpingooophorectomy. During the surgery, the mass was noted to be contiguous with the left obturator nerve. Pathologic evaluation revealed a schwannoma (WHO grade I). The patient's postsurgical course was uneventful, without residual weakness in the left adductor muscles.