Citations

Abstract Background and Objectives Artificial intelligence (AI) has been gaining increasing interest in healthcare. During the 2024 International Society of Blood Transfusion (ISBT) Congress, the Clinical Transfusion Working Party (CTWP) conducted a session to explore the exciting intersection of AI in transfusion medicine (TM) practice, education and research. We report here the potential applications and the session outcome. Materials and Methods A pre‐workshop survey explored the participants' demographics and areas of use of AI and whether they have had any AI‐specific training or education. The workshop included presentations on the regulatory aspects of AI use and its application in TM practice, education and research. These were followed by round‐table discussions to explore participants' experience and concerns. Results The workshop had 72 attendees, with 38% falling in the 36–45‐year age group. A total of 70% indicated the use of AI, but only 12% reported having specific training or education. Participants expressed interest in different potential applications but also shared concerns on over‐reliance, potential loss of skills, the accuracy of provided information and content plagiarism. Conclusion The findings of the workshop highlight the need for training, educational resources, standards and regulatory frameworks to guide the use of AI tools in the field of TM.

Dermatofibrosarcoma protuberans (DFSP) is a rare cutaneous soft tissue sarcoma and affects an estimated 1,500 people annually in the United States. DFSP frequently exhibits extensive local infiltration. Initial treatment is through surgical excision, and care should be taken to ensure that negative margins are achieved to minimize recurrence. Although DFSP has a reported high rate of recurrence, metastasis is more uncommon. Fibrosarcomatous DFSP is an aggressive variant with an increased risk for local recurrence and metastasis. If achieving negative margins or resection is not feasible, radiation therapy or systemic treatment are options that may be considered by a multidisciplinary team. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) outline recommended treatment options available for DFSP.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Lung Cancer Screening provide criteria for selecting individuals for screening and offer recommendations for evaluating and managing lung nodules detected during initial and subsequent annual screening. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines for Lung Cancer Screening.

AbstractEUS-guided gallbladder drainage (EUS-GBD) using lumen apposing metal stents (LAMS) has excellent technical and short-term clinical success for acute cholecystitis (AC). The goals of this study were to determine the long-term clinical outcomes and adverse events (AEs) of EUS-GBD with LAMS.A multicenter, retrospective study was conducted at 18 US tertiary care institutions. Inclusion criteria: any AC patient with attempted EUS-GBD with LAMS and minimum 30-day post-procedure follow-up. Long-term clinical success was defined as absence of recurrent acute cholecystitis (RAC) > 30 days and long-term AE was defined as occurring > 30 days from the index procedure.A total of 109 patients were included. Technical success was achieved in 108 of 109 (99.1%) and initial clinical success in 106 of 109 (97.2%). Long-term clinical success was achieved in 98 of 109 (89.9%) over a median follow-up of 140 days (range 30–1188). On multivariable analysis (MVA), acalculous cholecystitis (odds ratio [OR] 15.93, 95% confidence interval [CI] 1.22–208.52, P = 0.04) and the occurrence of a LAMS-specific AE (OR 63.60, 95% CI 5.08–799.29, P <0.01) were associated with RAC. AEs occurred in 38 of 109 patients (34.9%) at any time, and in 10 of 109 (9.17%) > 30 days from the index procedure. Most long-term AEs (7 of 109; 6.42%) were LAMS-specific. No technical or clinical factors were associated with occurrence of AEs. LAMS were removed in 24 of 109 patients (22%). There was no difference in RAC or AEs whether LAMS was removed or not.EUS-GBD with LAMS has a high rate of long-term clinical success and modest AE rates in patients with AC and is a reasonable destination therapy for high-risk surgical candidates.

ABSTRACTBackground and AimsChronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is difficult to distinguish from mimicking disorders, with misdiagnosis resulting in IVIG overutilization. We evaluate a clinical‐electrophysiological model to facilitate CIDP versus mimic neuropathy prediction.MethodsUsing the European Academy of Neurology/Peripheral Nerve Society (EAN/PNS) 2021 CIDP guidelines we derived 26 clinical and 144 nerve conduction variables. The model was generated and validated utilizing total CIDP (n = 129) and mimics (n = 309); including (1) IgG4‐nodopathies; (2) POEMS (polyneuropathy–organomegaly–endocrinopathy–monoclonal protein‐skin changes); (3) anti‐myelin‐associated‐glycoprotein; (4) paraneoplastic; (5) Waldenström B‐cell lymphoma; (6) diabetic neuropathies; (7) amyloidosis; (8) Charcot–Marie–Tooth; (9) motor neuropathies/neuronopathies; and (10) idiopathic‐inflammatory‐myopathies.ResultsWe analyzed 9282 clinical and 51 408 electrophysiological data points. Univariate analysis identified 11 of 26 clinical variables with significant odds ratios. A multivariate regression model using four clinical and two electrophysiologic variables achieved 93% area‐under‐curve (95% CI 91–95): progression over 8 weeks (OR 40.66, 95% CI 5.31–311.36), absent autonomic involvement (OR 17.82, 95% CI 2.93–108.24), absent muscle atrophy (OR 16.65, 95% CI 3.27–84.73), proximal weakness (OR 3.63, 95% CI 1.58–8.33), ulnar motor conduction velocity slowing < 35.7 m/s (OR 5.21, 95% CI 2.13–12.76), and ulnar motor conduction block (OR 13.37, 95% CI 2.47–72.40). A web‐based probability calculator (https://news.mayocliniclabs.com/cidp‐calculator/) was developed, with 100% sensitivity and 68% specificity at a 92% probability threshold. Specificity improved to 93% when considering “red flags,” electrophysiologic criteria, and laboratory testing.InterpretationA probability calculator using clinical electrophysiological variables assists CIDP differentiation from mimics, with scores below 92% unlikely to have CIDP. The highest specificity is achieved by considering clinical “red flags,” electrophysiologic demyelination, and laboratory testing.

AbstractBackgroundCross‐modality translation between MRI and PET presents significant challenges due to the distinct mechanisms underlying those modalities. Currently, the leading method for this translation is CycleGAN. Previous studies have proved a strong correlation between BBBMs and brain amyloid status measured by PET. However, the impact of blood‐based biomarkers (BBBMs) on PET image synthesis has not yet been thoroughly evaluated. In this study, we propose Plasma CycleGAN, a generative model based on CycleGAN to synthesize PET images from MRI, while also incorporating BBBMs and other clinical covariates. We show that Plasma CycleGAN outperforms the state‐of‐the‐art CycleGAN for MRI‐to‐PET translation. Our method is the first to integrate BBBMs in a conditional cross modality translation from MRI to PET.MethodOur model was built on CycleGAN architecture, while the backbone was improved by integrating BBBMs as additional conditions in the input domain. To be specific, we introduce a masked perturbation in the MRI image, whose voxel‐level magnitudes are learnable parameters in the neural network. The dataset we used to evaluate our model is downloaded from the ADNI database. The cohort contains 160 subjects with matching BBBM, T1‐weighted MRI images, amyloid PET, and clinical features, including age, sex, weights, etc. The synthesized PET images are compared with the ground truth PET images for performance evaluation using traditional metrics like structural similarity index (SSIM), peak‐signal‐to‐noise ratio (PSNR) and mean square error (MSE). In addition, the Pearson correlation coefficients (PCCs) of the masked SUVR ratios were calculated.ResultWe evaluated the baseline CycleGAN model using MRI input, as well as combined inputs of MRI+BBBM and other clinical features. Our model achieved an SSIM of 0.72±0.16 and PSNR of 22.8±2.3 in PET image synthesis task using MRI+ Aβ42/40, both yielding the best results among all input combinations. MRI+sex achieved the best result using MSE measurement. PCCs of all experimental results ranged from 0.44 to 0.5.ConclusionWe introduce Plasma CycleGAN, the first generative model to integrate BBBMs and clinical features in PET image synthesis from MRI images. In PET image synthesis task, our proposed integrated model outperforms the baseline CycleGAN model.