Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data
Authors: Canas Liane S. , Dong Trinh H. K. , Beasley Daniel , Donovan Joseph , Cleary Jon O. , Brown Richard , Thuong Nguyen Thuy Thuong , Nguyen Phu Hoan , Nguyen Ha Thi , Razavi Reza , Ourselin Sebastien , Thwaites Guy E. , Modat Marc ,   the Vietnam ICU Translational Applications Laboratory (VITAL) Investigators, Thao Dang Phuong , Kien Dang Trung , Thy Doan Bui Xuan , Trinh Dong Huu Khanh , Duc Du Hong , Geskus Ronald , Hai Ho Bich , Chanh Ho Quang , Van Hien Ho , Trieu Huynh Trung , Kestelyn Evelyne , Yen Lam Minh , Van Khoa Le Dinh , Phuong Le Thanh , Khanh Le Thuy Thuy , Tran Luu Hoai Bao , An Luu Phuoc , Mcbride Angela , Vuong Nguyen Lam , Huy Nguyen Quang , Quyen Nguyen Than Ha , Ngoc Nguyen Thanh , Giang Nguyen Thi , Trinh Nguyen Thi Diem , Le Thanh Nguyen Thi , Dung Nguyen Thi Phuong , Thao Nguyen Thi Phuong , Van Ninh Thi Thanh , Kieu Pham Tieu , Khanh Phan Nguyen Quoc , Lam Phung Khanh , Nhat Phung Tran Huy , Thwaites Guy , Thwaites Louise , Duc Tran Minh , Hung Trinh Manh , Turner Hugo , Van Nuil Jennifer Ilo , Hoang Vo Tan , Huyen Vu Ngo Thanh , Yacoub Sophie , Tam Cao Thi , Thuy Duong Bich , Duong Ha Thi Hai , Nghia Ho Dang Trung , Chau Le Buu , Toan Le Mau , Thu Le Ngoc Minh , Thao Le Thi Mai , Tai Luong Thi Hue , Phu Nguyen Hoan , Viet Nguyen Quoc , Dung Nguyen Thanh , Nguyen Nguyen Thanh , Phong Nguyen Thanh , Anh Nguyen Thi Kim , Van Hao Nguyen , Van Thanh Duoc Nguyen , Oanh Pham Kieu Nguyet , Van Phan Thi Hong , Qui Phan Tu , Tho Phan Vinh , Thao Truong Thi Phuong , Ali Natasha , Clifton David , English Mike , Hagenah Jannis , Lu Ping , McKnight Jacob , Paton Chris , Zhu Tingting , Georgiou Pantelis , Perez Bernard Hernandez , Hill-Cawthorne Kerri , Holmes Alison , Karolcik Stefan , Ming Damien , Moser Nicolas , Manzano Jesus Rodriguez , Canas Liane , Gomez Alberto , Kerdegari Hamideh , King Andrew , Modat Marc , Razavi Reza , Xochicale Miguel , Karlen Walter , Denehy Linda , Rollinson Thomas , Pisani Luigi , Schultz Marcus 
Published: July 2024

AbstractTuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other outcomes, especially when impacted by co-morbidities such as HIV infection. Brain magnetic resonance imaging (MRI) characterises the extent and severity of disease and may enable more accurate prediction of complications and poor outcomes. We analysed clinical and brain MRI data from a prospective longitudinal study of 216 adults with TBM; 73 (34%) were HIV-positive, a factor highly correlated with mortality. We implemented an end-to-end framework to model clinical and imaging features to predict disease progression. Our model used state-of-the-art machine learning models for automatic imaging feature encoding, and time-series models for forecasting, to predict TBM progression. The proposed approach is designed to be robust to missing data via a novel tailored model optimisation framework. Our model achieved a 60% balanced accuracy in predicting the prognosis of TBM patients over the six different classes. HIV status did not alter the performance of the models. Furthermore, our approach identified brain morphological lesions caused by TBM in both HIV and non-HIV-infected, associating lesions to the disease staging with an overall accuracy of 96%. These results suggest that the lesions caused by TBM are analogous in both populations, regardless of the severity of the disease. Lastly, our models correctly identified changes in disease symptomatology and severity in 80% of the cases. Our approach is the first attempt at predicting the prognosis of TBM by combining imaging and clinical data, via a machine learning model. The approach has the potential to accurately predict disease progression and enable timely clinical intervention.