Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Immune Checkpoint Inhibitor Toxicities

Immune checkpoint inhibitors are molecules that increase the endogenous immune response against tumors. They have revolutionized the field of oncology. Since their initial approval for the treatment of advanced melanoma, their use has expanded to the treatment of several other advanced cancers. Unfortunately, immune checkpoint inhibitors have also been associated with the emergence of a new subset of autoimmune-like toxicities, known as immune-related adverse events. These toxicities differ depending on the agent, malignancy, and individual susceptibilities. Although the skin and colon are most commonly involved, any organ may be affected, including the liver, lungs, kidneys, and heart. Most of these toxicities are diagnosed by excluding other secondary infectious or inflammatory causes. Corticosteroids are commonly used for treatment of moderate and severe immune-related adverse events, although additional immunosuppressive therapy may occasionally be required. The occurrence of immune-related toxicities may require discontinuation of immunotherapy, depending on the specific toxicity and its severity. In this article, we provide a focused review to familiarize practicing clinicians with this important topic given that the use of immune checkpoint inhibitors continues to increase.     

Combining CAPRA-S With Tumor IDC/C Features Improves the Prognostication of Biochemical Recurrence in Prostate Cancer Patients

Background: Intraductal carcinoma and cribriform (IDC/C) tumor features are well-established prognosticators of biochemical recurrence (BCR), metastasis, and prostate cancer (PCa)-specific mortality. However, approximately 70% of PCa patients undergoing a radical prostatectomy are IDC/C negative, yet up-to 20% of these patients progress and experience BCR. Thus, tumor histopathologic characteristics such as IDC/C alone are limited in their ability to predict disease progression. Conversely, several nomograms such as Cancer of the Prostate Risk Assessment-Surgery (CAPRA-S) have been developed to aid in the prognostication of BCR, but not yet widely applied in clinical settings. Materials and methods: In this study, we assessed the combined prognostic utility of IDC/C, and CAPRA-S for BCR in 3 PCa patient cohorts. Results: CAPRA-S+IDC/C improved the predictive accuracy of BCR in all 3 cohorts (P < .001). Specifically, among IDC/C negative cases, CAPRA-S improved the prognostication of BCR in low-risk (Cohort 1; P < .001, Cohort 2; P < .001, Cohort 3; P = .003), intermediate (Cohort 1; P < .001, Cohort 2; P = .006, Cohort 3; P = .03) and high-risk (Cohort 1-3; P < .001) patients. Conversely, IDC/C improved the prognostication of BCR among CAPRA-S low-risk (Cohorts 1; P < .001 and Cohort 3; P = .003) patients. Conclusion: Our results suggest the investigation of histopathological IDC/C features in CAPRA-S low-risk patients and conversely, nomogram CAPRA-S among IDC/C negative patients improves the identification of patients likely to experience BCR, which would otherwise be missed through current assessment regimens. These patients can be offered more intensive monitoring and adjuvant therapies upfront to circumvent the development of recurrent cancer or overtreatment at the time of surgery.     

Could non-HDL-cholesterol be a better marker of atherogenic dyslipidemia in obstructive sleep apnea?

Background/objectiveObstructive sleep apnea (OSA) is independently associated with dyslipidemia, a surrogate marker of atherosclerosis. Low-density lipoprotein (LDL)-cholesterol is accepted as a major independent risk factor for cardiovascular disease. However, non-high-density lipoprotein (HDL)-cholesterol is a better marker of atherogenic dyslipidemia and recommended as a target of lipid lowering therapy. We aimed to assess the prevalence of atherogenic dyslipidemia, and relationship between OSA severity and serum LDL-cholesterol and non-HDL cholesterol levels in OSA patients.MethodsWe retrospectively evaluated treatment naïve 2361 subjects admitted to the sleep laboratory of a university hospital for polysomnography. All subjects’ lipid profile including total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides, and non-HDL-cholesterol were measured.ResultsOut of 2361 patients (mean age 49.6 ± 11.9 years; 68.9% male, apnea-hypopnea index 36.6 ± 28.4/h), 185 (7.8%) had no OSA and 2176 (92.2%) had OSA. Atherogenic dyslipidemia prevalence was high (57–66%) in OSA patients, and especially increased in severe OSA compared to other groups (p < 0.05). Though total and LDL-cholesterol did not differ between those with and without OSA, non-HDL-cholesterol (p = 0.020), and triglycerides (p = 0.001) were higher and HDL-cholesterol levels (p = 0.018) were lower in OSA patients than non-OSA. Non-HDL-cholesterol was significantly correlated with OSA severity (p < 0.001) and hypoxia parameters (p < 0.01), whereas LDL-cholesterol showed no correlation.ConclusionsAtherogenic dyslipidemia is highly prevalent and non-HDL-cholesterol levels are significantly increased, predominantly in severe OSA patients. Non-HDL-cholesterol but not LDL-cholesterol, is significantly correlated with OSA severity and hypoxia parameters. Therefore, it could be better to use non-HDL-cholesterol, which is a guideline recommended target of lipid therapy, as a marker of atherosclerotic cardiovascular risk in OSA patients.     

Arachidonic acid-stimulated platelet tests: Identification of patients less sensitive to aspirin treatment

Serum thromboxane-B2 (TxB2), together with arachidonic acid (AA)-induced platelet aggregation, are, at the moment, the most used tests to identify patients displaying high on-aspirin treatment platelet reactivity (HAPR). Both tests are specific for aspirin action on cyclooxygenase-1. While the correlation between serum TxB2 assay and clinical outcome is established, data are conflicting with regard to aspirin treatment and a possible association with AA-stimulated platelet markers and clinical outcome. To understand such discrepancy, we performed a retrospective study to compare both assays. We collected data from 132 patients receiving a daily dose of aspirin (100?mg/day) and data from 48 patients receiving aspirin on alternate days. All Patients who received a daily dose of aspirin were studied for AA-induced platelet aggregation together with serum TxB2 levels and AA-induced TxB2 formation was also studied in 71 patients out of entire population. Consistent with recommendations in the literature, we defined HAPR by setting a cut-off point at 3.1?ng/ml for serum levels of thromboxane B2 and 20% for AA-induced platelet aggregation. According to this cut-off point, we divided our overall population into two groups: (1) TxB2?<?3.1?ng/ml and (2) TxB2?>?3.1?ng/ml. We found low agreement between such tests to identify patients displaying HAPR. Our results show that AA-induced platelet aggregation >20% identify a smaller number of HAPR patients in comparison with TxB2. A good correlation between serum TxB2 and arachidonic acid-induced TxB2 production was found (r?=?0.76619).