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.
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.
METHODS: A questionnaire survey was conducted before and after explanation of fertility issues and FP treatments for patients 6-17 years old who visited or were hospitalized for the purpose of ovarian tissue cryopreservation (OTC) or oocyte cryopreservation (OC), or sperm cryopreservation between October 2018 and April 2022. This study was approved by the institutional review board at St. Marianna University School of Medicine (No. 4123, UMIN000046125).
RESULT: Participants in the study comprised 36 children (34 girls, 2 boys). Overall mean age was 13.3 ± 3.0 years. The underlying diseases were diverse, with leukemia in 14 patients (38.9%), brain tumor in 4 patients (11.1%). The questionnaire survey before the explanation showed that 19 patients (52.8%) wanted to have children in the future, but 15 (41.7%) were unsure of future wishes to raise children. And most children expressed some degree of understanding of the treatment being planned for the underlying disease (34, 94.4%). Similarly, most children understood that the treatment would affect their fertility (33, 91.7%). When asked if they would like to hear a story about how to become a mother or father after FP which including information of FP, half answered "Don't mind" (18, 50.0%). After being provided with information about FP treatment, all participants answered that they understood the adverse effects on fertility of treatments for the underlying disease. Regarding FP treatment, 32 children (88.9%) expressed understanding for FP and 26 (72.2%) wished to receive FP. "Fear" and "Pain" and "Costs" were frequently cited as concerns about FP. Following explanations, 33 children (91.7%) answered "Happy I heard the story" and no children answered, "Wish I hadn't heard the story". Finally, 28 of the 34 girls (82.4%) underwent OTC and one girl underwent OC.
DISCUSSION: The fact that all patients responded positively to the explanations of FP treatment is very informative. This is considered largely attributable to the patients themselves being involved in the decision-making process for FP.
CONCLUSIONS: Explanations of FP for children appear valid if age-appropriate explanations are provided.