AIMS: To describe the national state of advance care planning development in Malaysia METHODS: Review of relevant advance care planning literature locally and internationally was undertaken.

RESULTS: Positive development in Malaysia includes implementation of advance care planning at institutional level, initiatives to develop educational programmes as well as research activities to understand the attitude and perception of patients on advance care planning. However, there remain challenges, including lack of knowledge and awareness, lack of legislative framework to guide advance care planning implementation and lack of strong initiatives at a national level.

PURPOSE: To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains. A systematic approach is proposed to prevent catastrophic forgetting during co-training.

STUDY TYPE: Retrospective.

POPULATION: A total of 156 patients (105 ground truth and 51 pseudo labels) with 1502 BM (BrainMetShare); 121 patients with 722 BM (local); 400 patients with 447 primary gliomas (BrATS). Training/pseudo labels/validation data were distributed 84/51/21 (BrainMetShare). Training/validation data were split: 121/23 (local) and 375/25 (BrATS).

FIELD STRENGTH/SEQUENCE: A 5 T and 3 T/T1 spin-echo postcontrast (T1-gradient echo) (BrainMetShare), 3 T/T1 magnetization prepared rapid acquisition gradient echo postcontrast (T1-MPRAGE) (local), 0.5 T, 1 T, and 1.16 T/T1-weighted-fluid-attenuated inversion recovery (T1-FLAIR) (BrATS).

ASSESSMENT: The ground truth was manually segmented by two (BrainMetShare) and four (BrATS) radiologists and manually annotated by one (local) radiologist. Confidence and volume based domain adaptation (CAVEAT) method of co-training the three datasets on a 3D nonlocal convolutional neural network (CNN) architecture was implemented to detect BM.

STATISTICAL TESTS: The performance was evaluated using sensitivity and false positive rates per patient (FP/patient) and free receiver operating characteristic (FROC) analysis at seven predefined (1/8, 1/4, 1/2, 1, 2, 4, and 8) FPs per scan.

RESULTS: The sensitivity and FP/patient from a held-out set registered 0.811 at 2.952 FP/patient (BrainMetShare), 0.74 at 3.130 (local), and 0.723 at 2.240 (BrATS) using the CAVEAT approach with lesions as small as 1 mm being detected.

DATA CONCLUSION: Improved sensitivities at lower FP can be achieved by co-training datasets via the CAVEAT paradigm to address the problem of data sparsity.

SETTING: Cohort study.

PARTICIPANTS: Twelve biologically unrelated Malaysian-Chinese patients with congenital hypothyroidism were recruited in this study. All patients showed high thyrotropin and low free thyroxine levels at the time of diagnosis with proven presence of a thyroid gland.

PRIMARY OUTCOME MEASURE: Screening of the c.2268dup mutation in the TPO gene in all patients was carried out using a PCR-direct DNA sequencing method.

SECONDARY OUTCOME MEASURE: Further screening for mutations in other exonic regions of the TPO gene was carried out if the patient was a carrier of the c.2268dup mutation.

RESULTS: The c.2268dup mutation was detected in 4 of the 12 patients. Apart from the c.2268dup and a previously documented mutation (c.2647C>T), two novel TPO alterations, c.670_672del and c.1186C>T, were also detected in our patients. In silico analyses predicted that the novel alterations affect the structure/function of the TPO protein.