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  1. Ninomiya K, Arimura H, Chan WY, Tanaka K, Mizuno S, Muhammad Gowdh NF, et al.
    PLoS One, 2021;16(1):e0244354.
    PMID: 33428651 DOI: 10.1371/journal.pone.0244354
    OBJECTIVES: To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs).

    MATERIALS AND METHODS: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test.

    RESULTS: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29).

    CONCLUSION: The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.

  2. Ninomiya K, Arimura H, Tanaka K, Chan WY, Kabata Y, Mizuno S, et al.
    Comput Methods Programs Biomed, 2023 Jun;236:107544.
    PMID: 37148668 DOI: 10.1016/j.cmpb.2023.107544
    OBJECTIVES: To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes.

    METHODS: In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling.

    RESULTS: The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively.

    CONCLUSION: 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.

  3. Garderet L, Gras L, Koster L, Baaij L, Hamad N, Dsouza A, et al.
    Am J Hematol, 2024 Aug 19.
    PMID: 39158218 DOI: 10.1002/ajh.27451
    Autologous hematopoietic cell transplantation (AHCT) is a commonly used treatment in multiple myeloma (MM). However, real-world global demographic and outcome data are scarce. We collected data on baseline characteristics and outcomes from 61 725 patients with newly diagnosed MM who underwent upfront AHCT between 2013 and 2017 from nine national/international registries. The primary endpoint was overall survival (OS), and the secondary endpoints were progression-free survival (PFS), relapse incidence (RI) and non-relapse mortality (NRM). Median OS amounted to 90.2 months (95% CI 88.2-93.6) and median PFS 36.5 months (95% CI 36.1-37.0). At 24 months, cumulative RI was 33% (95% CI 32.5%-33.4%) and NRM was 2.5% (95% CI 2.3%-2.6%). In the multivariate analysis, superior outcomes were associated with younger age, IgG subtype, complete hematological response at auto-HCT, Karnofsky score of 100%, international staging scoring (ISS) stage 1, HCT-comorbidity index (CI) 0, standard cytogenetic risk, auto-HCT in recent years, and use of lenalidomide maintenance. There were differences in the baseline characteristics and outcomes between registries. While the NRM was 1%-3% at 12 months worldwide, the OS at 36 months was 69%-84%, RI at 12 months was 12%-24% and PFS at 36 months was 43%-63%. The variability in these outcomes is attributable to differences in patient and disease characteristics as well as the use of maintenance and macroeconomic factors. In conclusion, worldwide data indicate that AHCT in MM is a safe and effective therapy with an NRM of 1%-3% with considerable regional differences in OS, PFS, RI, and patient characteristics. Maintenance treatment post-AHCT had a beneficial effect on OS.
  4. Sekiguchi F, Tsurusaki Y, Okamoto N, Teik KW, Mizuno S, Suzumura H, et al.
    J Hum Genet, 2019 Dec;64(12):1173-1186.
    PMID: 31530938 DOI: 10.1038/s10038-019-0667-4
    Coffin-Siris syndrome (CSS, MIM#135900) is a congenital disorder characterized by coarse facial features, intellectual disability, and hypoplasia of the fifth digit and nails. Pathogenic variants for CSS have been found in genes encoding proteins in the BAF (BRG1-associated factor) chromatin-remodeling complex. To date, more than 150 CSS patients with pathogenic variants in nine BAF-related genes have been reported. We previously reported 71 patients of whom 39 had pathogenic variants. Since then, we have recruited an additional 182 CSS-suspected patients. We performed comprehensive genetic analysis on these 182 patients and on the previously unresolved 32 patients, targeting pathogenic single nucleotide variants, short insertions/deletions and copy number variations (CNVs). We confirmed 78 pathogenic variations in 78 patients. Pathogenic variations in ARID1B, SMARCB1, SMARCA4, ARID1A, SOX11, SMARCE1, and PHF6 were identified in 48, 8, 7, 6, 4, 1, and 1 patients, respectively. In addition, we found three CNVs including SMARCA2. Of particular note, we found a partial deletion of SMARCB1 in one CSS patient and we thoroughly investigated the resulting abnormal transcripts.
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