Abstract Background Immunofixation electrophoresis (IFE) is the standard method for confirming the presence of a monoclonal protein (M‐protein) at multiple myeloma (MM) diagnosis. IFE is also essential at assessment of complete response (CR) and stringent CR during treatment. As the CR assessment is influenced by daratumumab and isatuximab, HYDRASHIFT assays were developed. Methods Samples from patients under treatment that included daratumumab or isatuximab were tested and monitored by IFE on the HYDRASYS system using HYDRASHIFT assays (HYDRASYS/HYDRASHIFT) and by IFE on the Epalyzer2 system (Epalyzer). Results The IFE using HYDRASYS/HYDRASHIFT avoided a false positive caused by drug‐related IgG‐κ and contributed to accurate assessment of CR. Furthermore, HYDRASYS/HYDRASHIFT detected small M‐proteins at early relapse and detected free light chains (FLCs) in patients with renal impairment exhibiting high serum FLCs despite being often missed on Epalyzer. Conclusion Sensitivity and specificity of M‐protein detection vary greatly depending on the IFE system and reagents used.

目的:新型コロナウイルス感染症が世界中に蔓延し,本邦でも社会活動を制限する緊急事態宣言が発出された.本邦におけるコロナ禍の細胞診業務への影響を調査する.

BACKGROUND:Understanding the gene alteration status of primary lung cancers is important for determining treatment strategies, but gene testing is both time-consuming and costly, limiting its application in clinical practice. Here, potential therapeutic targets were selected by predicting gene alterations in cytologic specimens before conventional gene testing.METHODS:This was a retrospective study to develop a cytologic image-based gene alteration prediction model for primary lung cancer. Photomicroscopic images of cytology samples were collected and image patches were generated for analyses. Cancer-positive (n = 106) and cancer-negative (n = 32) samples were used to develop a neural network model for selecting cancer-positive images. Cancer-positive cases were randomly assigned to training (n = 77) and validation (n = 26) data sets. Another neural network model was developed to classify cancer images of the training data set into 4 groups: anaplastic lymphoma kinase (ALK)-fusion, epidermal growth factor receptor (EGFR), or Kirsten rat sarcoma viral oncogene homologue (KRAS) mutated groups, and other (None group), and images of the validation data set were classified. A decision algorithm to predict gene alteration for cases with 3 probability ranks was developed.RESULTS:The accuracy and precision for selecting cancer-positive patches were 0.945 and 0.991, respectively. Predictive accuracy for the EGFR and KRAS groups in the validation data set was ~0.95, whereas that for the ALK and None groups was ~0.75 and ~ 0.80, respectively. Gene status was correctly predicted in the probability rank A cases. The model extracted characteristic conventional cytologic findings in images and a novel specific feature was discovered for the EGFR group.CONCLUSIONS:A gene alteration prediction model for lung cancers by machine learning based on cytologic images was successfully developed.

背景:ランゲルハンス細胞肉腫(Langerhans cell sarcoma,以下 LCS)はきわめてまれな腫瘍である.今回,右上腕に発生した LCS を経験したので報告する.