METHODS: A prospective study spanning 27 months was conducted at the University Hospital, Kuala Lumpur. Serum CEA (Abbott IMx) and serum squamous cell carcinoma antigen (Abbott IMx) from patients clinically suspected of having primary carcinoma of the lung, were assayed using the microparticle enzyme immunoassay method.
RESULTS: Thirty seven cases of histologically confirmed primary lung carcinoma were studied. Of these, 17 were squamous cell carcinomas, 10 adenocarcinomas, nine small cell carcinomas, and one large cell carcinoma. The patients' ages ranged from 34-82 years. The male:female ratio was 3.6:1. Squamous cell carcinoma antigen was raised above the cutoff value of 1.5 ng/ml in 94.1% of squamous cell carcinomas, 20.0% of adenocarcinomas, and 11.1% of small cell carcinomas. By comparison, CEA was raised above the cutoff value of 3.0 ng/ml in 70.6% of squamous cell carcinomas, 77.8% of small cell carcinomas, and 100% of adenocarcinomas. CEA and squamous cell carcinoma antigen were not raised in the patient with large cell carcinoma and in 14 healthy volunteers. None of 15 patients with a variety of benign lung diseases showed a rise of CEA, while two patients--a 25 year old Indian woman with pneumonia and a 64 year old Malay man with bronchial asthma--had raised squamous cell carcinoma antigen values above the cutoff. Serum CEA and squamous cell carcinoma antigen values did not seem to correlate with stage or degree of differentiation of the tumours.
CONCLUSIONS: The findings suggest that CEA is a good general marker for carcinoma, particularly adenocarcinoma. In contrast, squamous cell carcinoma antigen is more specific for squamous carcinoma.
METHODS: An online/face-to-face, questionnaire-based survey of respiratory specialists and primary care physicians from eight Asian countries/region was carried out. The survey explored asthma control, inhaler selection, technique and use; physician-patient communications and asthma education. Inclusion criteria were >50% of practice time spent on direct patient care; and treated >30 patients with asthma per month, of which >60% were aged >12 years.
RESULTS: REALISE Asia (Phase 2) involved 375 physicians with average 15.9(±6.8) years of clinical experience. 89.1% of physicians reporting use of guidelines estimated that 53.2% of their patients have well-controlled (GINA-defined) asthma. Top consideration for inhaler choice was asthma severity (82.4%) and lowest, socio-economic status (32.5%). Then 54.7% of physicians checked their patients' inhaler techniques during consultations but 28.2(±19.1)% of patients were using their inhalers incorrectly; 21.1-57.9% of physicians could spot improper inhaler techniques in video demonstrations. And 79.6% of physicians believed combination inhalers could increase adherence because of convenience (53.7%), efficacy (52.7%) and usability (18.9%). Initial and follow-up consultations took 16.8(±8.4) and 9.2(±5.3) minutes, respectively. Most (85.1%) physicians used verbal conversations and least (24.5%), video demonstrations of inhaler use; 56.8% agreed that patient attitudes influenced their treatment approach.
CONCLUSION: Physicians and patients have different views of 'well-controlled' asthma. Although physicians informed patients about asthma and inhaler usage, they overestimated actual usage and patients' knowledge was sub-optimal. Physician-patient interactions can be augmented with understanding of patient attitudes, visual aids and ancillary support to perform physical demonstrations to improve treatment outcomes.
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.