Multimodality imaging in a case of multiple pulmonary hyalinizing granulomas - A decade follow-up.

A 44-year-old male was referred to our clinic (2015) to evaluate multiple lung nodules with increasing fatigue, dyspnea, and weight loss. He was being assessed to an outside hospital for the same since 2010. The X-ray and computed-tomography (CT)-chest showed numerous pulmonary nodules and bilateral hilar adenopathy. Imaging workup at our institute (2015) redemonstrated extensive calcified pulmonary nodules. 18fluoro-2-deoxy-d-glucose positron emission tomographyCT showed widespread pulmonary nodules with low-grade uptake. Video-assisted thoracic surgery lung biopsy revealed pulmonary hyalinizing granuloma (PHG). Recently because of increasing symptoms, he is being evaluated for a lung transplant. This case represents a rare diagnosis of PHG with a decade follow-up.

Role of FDG PET/CT for detection of primary tumor in patients with extracervical metastases from carcinoma of unknown primary.

AIM: To explore the diagnostic performance of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) to detect the primary tumor site in patients with extracervical metastases from carcinoma of unknown primary (CUP). We evaluated patient outcomes as overall survival (OS). MATERIALS AND METHODS: In a single-center, retrospective study (2005-2019), patients with extracervical metastases from CUP underwent FDG PET/CT to detect primary tumor sites. The final diagnosis was based on histopathology/or clinical follow-up of at least 12 months. RESULTS: A total of 83 patients [Male 41 (49%), mean age 59 ± 14 years, range: 32-83 years] fulfilled the inclusion/exclusion criteria and were enrolled for analysis. The primary tumor was detected in 36 out of 83 (43%) patients based on histopathology/or clinical follow-up. PET/CT suggested the primary tumor site in 39 (47%) patients with diagnostic accuracy of 87%, sensitivity 89%, specificity 85%, PPV 82%, NPV 91% and detection rate 39%. Patients with oligometastases (<3) (2.16 years, 1.04-2.54) and primary unidentified (1 year, 0.34-2.14) had longer median survival time compared to the patients with multiple metastases (0.67 years, 0.17-1.58, p = 0.009) and primary identified (0.67 years,0.16-1.33, p = 0.002). The SUVmax of the primary or metastatic lesions with maximum uptake was not significantly related to survival. CONCLUSIONS: PET/CT could reveal the primary tumor site in 39% of the patients. It demonstrated the metastatic disease burden and distribution in patients with 'primary obscured', which directs management. Patients with multiple metastases and primary identified had a poorer prognosis. In patients with primary unidentified after PET/CT, a further search was futile.

Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.

OBJECTIVES: Despite the robust diagnostic performance of MRI-based radiomic features for differentiating between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) reported on prior studies, the best sequence or a combination of sequences and model performance across various machine learning pipelines remain undefined. Herein, we compare the diagnostic performance of multiple radiomics-based models to differentiate GBM from PCNSL. METHODS: Our retrospective study included 94 patients (34 with PCNSL and 60 with GBM). Model performance was assessed using various MRI sequences across 45 possible model and feature selection combinations for nine different sequence permutations. Predictive performance was assessed using fivefold repeated cross-validation with five repeats. The best and worst performing models were compared to assess differences in performance. RESULTS: The predictive performance, both using individual and a combination of sequences, was fairly robust across multiple top performing models (AUC: 0.961-0.977) but did show considerable variation between the best and worst performing models. The top performing individual sequences had comparable performance to multiparametric models. The best prediction model in our study used a combination of ADC, FLAIR, and T1-CE achieving the highest AUC of 0.977, while the second ranked model used T1-CE and ADC, achieving a cross-validated AUC of 0.975. CONCLUSION: Radiomics-based predictive accuracy can vary considerably, based on the model and feature selection methods as well as the combination of sequences used. Also, models derived from limited sequences show performance comparable to those derived from all five sequences. KEY POINTS: • Radiomics-based diagnostic performance of various machine learning models for differentiating glioblastoma and PCNSL varies considerably. • ML models using limited or multiple MRI sequences can provide comparable performance, based on the chosen model. • Embedded feature selection models perform better than models using a priori feature reduction.

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study.

OBJECTIVES: To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. METHODS: Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. RESULTS: The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909-0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. CONCLUSIONS: T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

Chronic Venous Insufficiency Causing a Diagnostic Conundrum on Tc-99m Bone Scan Done for Osteosarcoma Surveillance: Awareness is the Key!

Bone scans are the most commonly used imaging technique to rule out local recurrence or metastasis during surveillance of malignant bone tumors after treatment. Although bone scans are very sensitive in detecting recurrence or metastasis, they are less specific. There are many nonmalignant conditions which can mimic either recurrence or metastasis on a Tc-99m bone scan. Therefore, physicians must be aware of such conditions to avoid unnecessary workup and invasive procedures. We present such an interesting case where chronic venous insufficiency mimicked either osteomyelitis or regional metastasis on a Tc-99m bone scan done for osteosarcoma surveillance.

Non Traumatic Spinal Epidural Haematoma in a Woman with HELLP Syndrome: A Case Report.

Spinal epidural haematomas (SEH) is a potentially reversible cause of spinal cord and nerve root compression which needs prompt surgical decompression for satisfactory neurological recovery. SEH occurs very rarely in pregnant woman with HELLP syndrome (hemolysis, elevated liver enzyme levels, and low platelet levels). Most of the SEH cases reported in HELLP syndrome in the literature are due to iatrogenic interventions. We report a still rarer case of non traumatic spinal epidural haematoma in a pregnant woman with HELLP Syndrome.