A retrospective multicentric cohort study of checkpoint inhibitors-induced pruritus with focus on management.

BACKGROUND: Limited data on immune checkpoint inhibitor (ICI)-induced pruritus per se and efficacy of different therapeutic modalities in its management exist. OBJECTIVE: To study the quantitative and qualitative characteristics of ICI-induced pruritus per se and to assess the efficacy of the therapeutic modalities usually applied. METHODS: We retrospectively reviewed the records of 91 patients who were under treatment with ICIs for any kind of neoplasia and developed pruritus during treatment. RESULTS: Twenty out of 91 individuals (22.0%) with ICI-induced pruritus had pruritus as the only symptom, while 71/91 (78.0%) presented with pruritus coexisting with an additional cutaneous toxicity. Pruritus was treated with antihistamines (18/20, 90.0%) and/or topical regimens, as first-line choice. In resistant cases, as a second therapeutic intervention, narrow-band UVB (NBUVB), oral steroids and GABA analogs were added (70.0%). Statistical analysis revealed a significant difference in mean pruritus Numerical Rating Scale (NRS) scores between baseline and sequential visits. Moreover, subgroup analysis revealed a significant reduction in mean NRS scores in those treated with phototherapy. LIMITATIONS: Retrospective design, low number of patients and survivorship bias. CONCLUSION: Pruritus per se was present in a substantial portion of our cohort (22.0%). Our study confirms the efficacy of current treatment strategies and suggests NBUVB as a potential steroid-sparing therapeutic alternative.

Artificial intelligence-assisted evaluation of cardiac function by oncology staff in chemotherapy patients.

Aims: Left ventricular ejection fraction (LVEF) calculation by echocardiography is pivotal in evaluating cancer patients' cardiac function. Artificial intelligence (AI) can facilitate the acquisition of optimal images and automated LVEF (autoEF) calculation. We sought to evaluate the feasibility and accuracy of LVEF calculation by oncology staff using an AI-enabled handheld ultrasound device (HUD). Methods and results: We studied 115 patients referred for echocardiographic LVEF estimation. All patients were scanned by a cardiologist using standard echocardiography (SE), and biplane Simpson's LVEF was the reference standard. Hands-on training using the Kosmos HUD was provided to the oncology staff before the study. Each patient was scanned by a cardiologist, a senior oncologist, an oncology resident, and a nurse using the TRIO AI and KOSMOS EF deep learning algorithms to obtain autoEF. The correlation between autoEF and SE-ejection fraction (EF) was excellent for the cardiologist (r = 0.90), the junior oncologist (r = 0.82), and the nurse (r = 0.84), and good for the senior oncologist (r = 0.79). The Bland-Altman analysis showed a small underestimation by autoEF compared with SE-EF. Detection of impaired LVEF < 50% was feasible with a sensitivity of 95% and specificity of 94% for the cardiologist; sensitivity of 86% and specificity of 93% for the senior oncologist; sensitivity of 95% and specificity of 91% for the junior oncologist; and sensitivity of 94% and specificity of 87% for the nurse. Conclusion: Automated LVEF calculation by oncology staff was feasible using AI-enabled HUD in a selected patient population. Detection of LVEF < 50% was possible with good accuracy. These findings show the potential to expedite the clinical workflow of cancer patients and speed up a referral when necessary.

The Impact of Expert Pathology Review and Molecular Diagnostics on the Management of Sarcoma Patients: A Prospective Study of the Hellenic Group of Sarcomas and Rare Cancers.

Precise classification of sarcomas is crucial to optimal clinical management. In this prospective, multicenter, observational study within the Hellenic Group of Sarcoma and Rare Cancers (HGSRC), we assessed the effect of expert pathology review, coupled with the application of molecular diagnostics, on the diagnosis and management of sarcoma patients. Newly diagnosed sarcoma patients were addressed by their physicians to one of the two sarcoma pathologists of HGSRC for histopathological diagnostic assessment. RNA next-generation sequencing was performed on all samples using a platform targeting 86 sarcoma gene fusions. Additional molecular methods were performed in the opinion of the expert pathologist. Therefore, the expert pathologist provided a final diagnosis based on the histopathological findings and, when necessary, molecular tests. In total, 128 specimens from 122 patients were assessed. Among the 119 cases in which there was a preliminary diagnosis by a non-sarcoma pathologist, there were 37 modifications in diagnosis (31.1%) by the sarcoma pathologist, resulting in 17 (14.2%) modifications in management. Among the 110 cases in which molecular tests were performed, there were 29 modifications in diagnosis (26.4%) through the genomic results, resulting in 12 (10.9%) modifications in management. Our study confirms that expert pathology review is of utmost importance for optimal sarcoma diagnosis and management and should be assisted by molecular methods in selected cases.