Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Background: and Purpose The management of acute ischemic stroke (AIS) due to distal medium vessel occlusion (DMVO) remains uncertain, particularly in comparing the effectiveness of intravenous thrombolysis (IVT) plus mechanical thrombectomy (MT) versus IVT alone. This study aimed to evaluate the safety and efficacy in DMVO patients treated with either MT-IVT or IVT alone. Methods: This multinational study analyzed data from 37 centers across North America, Asia, and Europe. Patients with AIS due to DMVO were included, with data collected from September 2017 to July 2023. The primary outcome was functional independence, with secondary outcomes including mortality and safety measures such as types of intracerebral hemorrhage. Results: The study involved 1,057 patients before matching, and 640 patients post-matching. Functional outcomes at 90 days showed no significant difference between groups in achieving good functional recovery (modified Rankin Scale 0–1 and 0–2), with adjusted odds ratios (OR) of 1.21 (95% confidence interval [CI] 0.81 to 1.79; P=0.35) and 1.00 (95% CI 0.66 to 1.51; P>0.99), respectively. Mortality rates at 90 days were similar between the two groups (OR 0.75, 95% CI 0.44 to 1.29; P=0.30). The incidence of symptomatic intracerebral hemorrhage was comparable, but any type of intracranial hemorrhage was significantly higher in the MT-IVT group (OR 0.43, 95% CI 0.29 to 0.63; P<0.001). Conclusion The results of this study indicate that while MT-IVT and IVT alone show similar functional and mortality outcomes in DMVO patients, MT-IVT presents a higher risk of hemorrhagic complications, thus MT-IVT may not routinely offer additional benefits over IVT alone for all DMVO stroke patients. Further prospective randomized trials are needed to identify patient subgroups most likely to benefit from MT-IVT treatment in DMVO.

Recent reports of natural human infection by Plasmodium cynomolgi indicate the increased risk of zoonotic transmission by this simian parasite. The P. cynomolgi Duffy binding protein 2 (PcDBP2) has a potential role in the invasion pathway of host erythrocytes, and it is a possible vaccine candidate against cynomolgi malaria. This study investigates the genetic diversity, haplotypes, and natural selection of PcDBP2 region II from isolates collected from wild macaques in Peninsular Malaysia. Blood samples from 50 P. cynomolgi-infected wild macaques were used in the study. Genomic DNA extracted from the blood samples was used as template for PCR amplification of the PcDBP2 region II. The amplicons were cloned into a plasmid vector and sequenced. MEGA X and DnaSP ver.6.12.03 programmes were used to analyse the DNA sequences. A genealogical relationship of PcDBP2 region II were determined using haplotype network tree on NETWORK ver.10.2. Result showed high genetic diversity (ð = 0.017 ± 0.002; Hd = 1.000 ± 0.001) of the PcDBP2 region II. The Z-test indicates a purifying selection, with population expansion as shown in Tajima’s D analysis. A total of 146 haplotypes of PcDBP2 region II were observed. Phylogenetic tree analysis showed that these haplotypes were grouped into three allelic types (136 for Strain B type, 9 for Berok type, and 1 recombinant type). In the haplotype network, PcDBP2 region II revealed no geographical groupings but was divided into two distinct clusters.

Osteosarcoma is the most frequent primary malignant mesenchymal bone tumour in children and adolescents. Although the lung is the most common site of its metastasis, to the best of our knowledge, it is infrequent to have hypervascular pulmonary metastasis, particularly in the post-operative period. Herein, we report a case of a 15-year- old boy who presented with a rapidly enlarging lung mass on a background of osteosarcoma of left proximal tibia. The progressively enlarging right lung mass was detected as an opacity on a chest radiograph, three months post-surgical resection of the osteosarcoma. Computed tomography of thorax revealed a contrast-enhancing hypervascular right lung mass. This was complicated with intra-lesional haemorrhage post-biopsy. Histopathological examination (HPE) confirmed metastatic osteosarcoma. We discuss the rarity of this occurrence and its imaging findings.
Osteosarcoma

In recent years, increasing cases of Plasmodium vivax complications had been reported all over the world. This former benign Plasmodium species is now recognized to be one of the human malaria parasites that can produce severe disease. In this article, we report two cases of sub-microscopic P. vivax malaria confirmed by PCR. Both patients were asymptomatic before treatment. They showed unusual presentations few days after initiation of antimalarial treatment. Both patients had subsequently completed antimalarial treatment and recovered completely.

Through the regional control programme, Malaysia has been successfully reducing the incidence of Plasmodium falciparum and Plasmodium vivax infections. However, the incidence of zoonotic malaria Plasmodium knowlesi infection is increasing and now has been the major cause of malaria in Malaysia especially Malaysian Borneo. The emergence of knowlesi infection has threatened the malaria elimination programme which the government aims to reduce the overall malaria infections by 2020. Unlike other benign human Plasmodium spp., P. knowlesi can cause fatal infections. The aim of this study was to determine the incidence and distribution of five human malaria parasites including P. knowlesi in Peninsular Malaysia and Malaysian Borneo. A total of 112 blood samples were collected from seven states and district hospitals in Peninsular Malaysia and Malaysian Borneo from year 2015 to 2016. The samples were examined by microscopy and further confirmed by nested PCR assay targeting 18S rRNA gene of Plasmodium spp. Following the nested PCR assays, a total of 54 (48.2%) samples were positive for P. knowlesi infections, 12 (10.7%) cases were positive for P. vivax infections, followed by 7 (6.3%) cases of P. falciparum and 4 (3.5%) cases of P. malariae. There were 3 cases (2.7%) of mixed infections (P. knowlesi/P. vivax). However, no cases were identified as P. ovale. A total of 32 (28.6%) cases were found as negative infections. LoopMediated Isothermal Amplification Assay (LAMP) was performed to confirm inconclusive results produced by microscopy and nested PCR. P. knowlesi showed the highest prevalence in Sarawak (n= 30), Sabah (n=13), Pulau Pinang (n=5) and Pahang (n=6). PCR and LAMP was not able to detect a large number of microscopy positive samples due to DNA degradation during storage and shipping. Among all the states involved in this study, the highest prevalence of P. knowlesi infection was found in Sabah and Sarawak.