Cross-sectional imaging of pancreatic leak: a pictorial review.

Pancreatic leaks occur when a disruption in the pancreatic ductal system results in the leakage of pancreatic enzymes such as amylase, lipase, and proteases into the abdominal cavity. While often associated with pancreatic surgical procedures, trauma and necrotizing pancreatitis are also common culprits. Cross-sectional imaging, particularly computed tomography, plays a crucial role in assessing postoperative conditions and identifying both early and late complications, including pancreatic leaks. The presence of fluid accumulation or hemorrhage near an anastomotic site strongly indicates a pancreatic fistula, particularly if the fluid is connected to the pancreatic duct or anastomotic suture line. Pancreatic fistulas are a type of pancreatic leak that carries a high morbidity rate. Early diagnosis and assessment of pancreatic leaks require vigilance and an understanding of its imaging hallmarks to facilitate prompt treatment and improve patient outcomes. Radiologists must maintain vigilance and understand the imaging patterns of pancreatic leaks to enhance diagnostic accuracy. Ongoing improvements in surgical techniques and diagnostic approaches are promising for minimizing the prevalence and adverse effects of pancreatic fistulas. In this pictorial review, our aim is to facilitate for radiologists the comprehension of pancreatic leaks and their essential imaging patterns.

Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis.

BACKGROUND: The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. METHODS: In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1-3 vs 4-5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. FINDINGS: In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942-0·956), accuracy of 0·890 (0·879-0·901), sensitivity of 0·868 (0·851-0·885), and specificity of 0·913 (0·899-0·925); LARS-max achieved an AUC of 0·949 (0·942-0·956), accuracy of 0·868 (0·858-0·879), sensitivity of 0·909 (0·896-0·924), and specificity of 0·826 (0·808-0·843); and LARS-ptct achieved an AUC of 0·939 (0·930-0·948), accuracy of 0·875 (0·864-0·887), sensitivity of 0·836 (0·817-0·855), and specificity of 0·915 (0·901-0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938-0·966), accuracy of 0·907 (0·888-0·925), sensitivity of 0·874 (0·843-0·904), and specificity of 0·949 (0·921-0·960); LARS-max achieved an AUC of 0·952 (0·937-0·965), accuracy of 0·898 (0·878-0·916), sensitivity of 0·899 (0·871-0·926), and specificity of 0·897 (0·871-0·922); and LARS-ptct achieved an AUC of 0·932 (0·915-0·948), accuracy of 0·870 (0·850-0·891), sensitivity of 0·827 (0·793-0·863), and specificity of 0·913 (0·889-0·937). INTERPRETATION: Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. FUNDING: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.

CT Imaging Assessment of Pancreatic Adenocarcinoma Resectability after Neoadjuvant Therapy: Current Status and Perspective on the Use of Radiomics.

Pancreatic adenocarcinoma (PDAC) is the most common pancreatic cancer and is associated with poor prognosis, a high mortality rate, and a substantial number of healthy life years lost. Surgical resection is the primary treatment option for patients with resectable disease; however, only 10-20% of all patients with PDAC are eligible for resection at the time of diagnosis. In this context, neoadjuvant therapy has the potential to increase the number of patients who are eligible for resection, thereby improving the overall survival rate. For patients who undergo neoadjuvant therapy, computed tomography (CT) remains the primary imaging tool for assessing treatment response. Nevertheless, the interpretation of imaging findings in this context remains challenging, given the similarity between viable tumor and treatment-related changes following neoadjuvant therapy. In this review, following an overview of the various treatment options for PDAC according to its resectability status, we will describe the key challenges regarding CT-based evaluation of PDAC treatment response following neoadjuvant therapy, as well as summarize the literature on CT-based evaluation of PDAC treatment response, including the use of radiomics. Finally, we will outline key recommendations for the management of PDAC after neoadjuvant therapy, taking into consideration CT-based findings.

Applying Natural Language Processing to Single-Report Prediction of Metastatic Disease Response Using the OR-RADS Lexicon.

Generating Real World Evidence (RWE) on disease responses from radiological reports is important for understanding cancer treatment effectiveness and developing personalized treatment. A lack of standardization in reporting among radiologists impacts the feasibility of large-scale interpretation of disease response. This study examines the utility of applying natural language processing (NLP) to the large-scale interpretation of disease responses using a standardized oncologic response lexicon (OR-RADS) to facilitate RWE collection. Radiologists annotated 3503 retrospectively collected clinical impressions from radiological reports across several cancer types with one of seven OR-RADS categories. A Bidirectional Encoder Representations from Transformers (BERT) model was trained on this dataset with an 80-20% train/test split to perform multiclass and single-class classification tasks using the OR-RADS. Radiologists also performed the classification to compare human and model performance. The model achieved accuracies from 95 to 99% across all classification tasks, performing better in single-class tasks compared to the multiclass task and producing minimal misclassifications, which pertained mostly to overpredicting the equivocal and mixed OR-RADS labels. Human accuracy ranged from 74 to 93% across all classification tasks, performing better on single-class tasks. This study demonstrates the feasibility of the BERT NLP model in predicting disease response in cancer patients, exceeding human performance, and encourages the use of the standardized OR-RADS lexicon to improve large-scale prediction accuracy.

Prolonged response with bevacizumab in a patient with benign metastasizing leiomyomatosis.

•Benign metastasizing leiomyomatosis (BML) is a rare condition.•Treatment options have traditionally included surgical resection or hormonal based therapy.•Traditional chemotherapy for BML is ineffective.•Our patient has had a prolonged response of stable disease with the anti-VEGF therapy of bevacizumab.

Rare Presentation of Burned-Out Testicular Seminoma.

A 28 year old male who complained of abdominal pain over the past several months was found on CT to have lymphadenopathy along the right aspect of the inferior vena cava. The patient was subsequently seen by an oncologist where further work up of the lymphadenopathy was performed. A MR of the abdomen demonstrated right aortocaval and para-caval lymph nodes measuring to 3.7cm. A testicular ultrasound was then performed, which demonstrated an apparent peripheral focal hypoechoic region with no associated internal vascularity within the right testes. Biopsy of the retroperitoneal lymph nodes pathologically confirmed the diagnosis of seminoma. Keywords: Seminoma, Testicular Cancer, Retroperitoneal Adenopathy, Oncology, Burned-Out Tumor.

Retro-tracheal parathyroid adenoma: A rare location of a common pathology.

Ectopic parathyroid adenoma in the mediastinum has been reported in several publications; however, its location in the posterior mediastinum, especially a retro-tracheal location, has been rarely reported. We report a case of a 61-year-old patient who presented with clinical symptoms of malignant hypercalcemia due to a retro-tracheal mediastinal parathyroid adenoma. The surgical excision normalized the phosphocalcic balance with improvement in the patient's clinical symptoms. An ectopic hypersecreting parathyroid adenoma with life-threatening hypercalcemia should prompt radiological assessment and appropriate surgical management to prevent significant clinical complications.

Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.

PURPOSE: To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver. METHODS: We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time. RESULTS: Simulations showed good agreement between LLS and NLLS for a range of kinetic parameters. The whole-field LLS method allowed generating liver perfusion maps approximately 160-fold faster than voxel-wise NLLS, while obtaining similar perfusion parameters. CONCLUSIONS: Delay-compensated dual-input liver perfusion analysis using whole-field LLS allows generating perfusion maps with a considerable speedup compared with conventional voxel-wise NLLS fitting. Magn Reson Med 79:2415-2421, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease: Prevalence and Association with PKD2 Gene Mutations.

Purpose To define the magnetic resonance (MR) imaging prevalence of pancreatic cysts in a cohort of patients with autosomal dominant polycystic kidney disease (ADPKD) compared with a control group without ADPKD that was matched for age, sex, and renal function. Materials and Methods In this HIPAA-compliant, institutional review board-approved study, all patients with ADPKD provided informed consent; for control subjects, informed consent was waived. Patients with ADPKD (n = 110) with mutations identified in PKD1 or PKD2 and control subjects without ADPKD or known pancreatic disease (n = 110) who were matched for age, sex, estimated glomerular filtration rate, and date of MR imaging examination were evaluated for pancreatic cysts by using axial and coronal single-shot fast spin-echo T2-weighted images obtained at 1.5 T. Total kidney volume and liver volume were measured. Univariate and multivariable logistic regression analyses were conducted to evaluate potential associations between collected variables and presence of pancreatic cysts among patients with ADPKD. The number, size, location, and imaging characteristics of the cysts were recorded. Results Patients with ADPKD were significantly more likely than control subjects to have at least one pancreatic cyst (40 of 110 patients [36%] vs 25 of 110 control subjects [23%]; P = .027). In a univariate analysis, pancreatic cysts were more prevalent in patients with ADPKD with mutations in PKD2 than in PKD1 (21 of 34 patients [62%] vs 19 of 76 patients [25%]; P = .0002). In a multivariable logistic regression model, PKD2 mutation locus was significantly associated with the presence of pancreatic cysts (P = .0004) and with liver volume (P = .038). Patients with ADPKD and a pancreatic cyst were 5.9 times more likely to have a PKD2 mutation than a PKD1 mutation after adjusting for age, race, sex, estimated glomerular filtration rate, liver volume, and total kidney volume. Conclusion Pancreatic cysts were more prevalent in patients with ADPKD with PKD2 mutation than in control subjects or patients with PKD1 mutation. (©) RSNA, 2016 Online supplemental material is available for this article.

Vitamin D deficiency: implications for acute care in the elderly and in patients with chronic illness.

There is evidence that the vast majority of hospitalized patients have vitamin D deficiency. Vitamin D deficiency is a poorly recognized pandemic with evidence to indicate inadequate testing and monitoring of response to treatment in high-risk populations. Vitamin D receptors are ubiquitous in the human body and while the endocrine effects of vitamin D are well recognized, the autocrine and paracrine effects of this steroid hormone are less well appreciated. These functions include antimicrobial and immunomodulation effects as well benefits on cardiovascular health, autoimmune disease, cancer and metabolism. Vitamin D deficiency increases mortality and even a modest amount of vitamin D may enhance longevity. Emerging evidence suggests that a vitamin D replete state carries significant health benefits in acute illness. In this review, we discuss the role of vitamin D deficiency and potential benefits in treating this deficiency focusing on the implications for managing acute illness in elderly patients and those with an underlying chronic illness.

Clinical inquiries: What measures relieve postherpetic neuralgia?

EVIDENCE-BASED ANSWER: Tricyclic antidepressants, gabapentin, and pregabalin effectively reduce pain. Opioids have demonstrated pain relief in 3 RCTs. Capsaicin and the lidocaine 5% patch relieve pain and decrease allodynia.

Methadone.

Methadone hydrochloride is an old drug that has been in vogue off and on. It has complex pharmacodynamics and can be potentially fatal in inexperienced settings. Drug switching from an opioid to methadone or vice versa requires knowledge of equianalgesic dosing. It is critical when using the drug to monitor for signs and symptoms of toxicity so that overdosing or toxicity can be identified in a timely manner. This review discusses these important topics so that methadone can be used safely and effectively.