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Booth TC, Chelliah A, Roman A, Al Busaidi A, Shuaib H, Luis A, Mirchandani A, Alparslan B, Mansoor N, Ashkan K, Ourselin S, Modat M, Grzeda M. OS08.6.A Glioblastoma treatment response machine learning monitoring biomarkers: a systematic review and meta-analysis. Neuro Oncol 2021. [DOI: 10.1093/neuonc/noab180.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
BACKGROUND
The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML).
MATERIAL AND METHODS
PRISMA methodology was followed. Articles published 09/2018-01/2021 (since previous reviews) were searched for using MEDLINE, EMBASE, and the Cochrane Register by two reviewers independently. Included study participants were adult patients with high grade glioma who had undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide) and subsequently underwent follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics - the target condition). Risk of bias and applicability was assessed with QUADAS 2. A third reviewer arbitrated any discrepancy. Contingency tables were created for hold-out test sets and recall, specificity, precision, F1-score, balanced accuracy calculated. A meta-analysis was performed using a bivariate model for recall, false positive rate and area-under the receiver operator characteristic curve (AUC).
RESULTS
Eighteen studies were included with 1335 patients in training sets and 384 in test sets. To determine whether there was progression or a mimic, the reference standard combination of follow-up imaging and histopathology at re-operation was applied in 67% (13/18) of studies. The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data. Ten studies (10/18, 56%) had internal or external hold-out test set data that could be included in a meta-analysis of monitoring biomarker studies. The pooled sensitivity was 0.77 (0.65–0.86). The pooled false positive rate (1-specificity) was 0.35 (0.25–0.47). The summary point estimate for the AUC was 0.77.
CONCLUSION
There is likely good diagnostic performance of machine learning models that use MRI features to distinguish between progression and mimics. The diagnostic performance of ML using implicit features did not appear to be superior to ML using explicit features. There are a range of ML-based solutions poised to become treatment response monitoring biomarkers for glioblastoma. To achieve this, the development and validation of ML models require large, well-annotated datasets where the potential for confounding in the study design has been carefully considered. Therefore, multidisciplinary efforts and multicentre collaborations are necessary.
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Affiliation(s)
- T C Booth
- King’s College London, London, United Kingdom
| | - A Chelliah
- King’s College London, London, United Kingdom
| | - A Roman
- Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom
| | - A Al Busaidi
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - H Shuaib
- Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom
| | - A Luis
- King’s College London, London, United Kingdom
| | - A Mirchandani
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - B Alparslan
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - N Mansoor
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - K Ashkan
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - S Ourselin
- King’s College London, London, United Kingdom
| | - M Modat
- King’s College London, London, United Kingdom
| | - M Grzeda
- King’s College London, London, United Kingdom
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Farooqi K, Chelliah A, Chai P, Bacha E, Saeed O, Jorde U, Einstein A. Impact of Pre-Procedural Planning with 3D Printed Models on Patient Outcomes for Ventricular Assist Device Placement in Adults with Congenital Heart Disease: Rationale and Design of a Multicenter Prospective Registry. J Heart Lung Transplant 2018. [DOI: 10.1016/j.healun.2018.01.471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
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Schwenger K, Chin L, Chelliah A, Da Silva H, teterina A, Comelli E, Taibi A, Arendt B, Fischer S, Allard J. A1 MARKERS OF ACTIVATED INFLAMMATORY CELLS ARE ASSOCIATED WITH NON-ALCOHOLIC FATTY LIVER DISEASE AND INTESTINAL MICROBIOTA. J Can Assoc Gastroenterol 2018. [DOI: 10.1093/jcag/gwy009.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
| | - L Chin
- Department of Pathology and Molecular Medicine, Kingston, ON, Canada
| | | | - H Da Silva
- Sunnybrook Health Sciences Center, Toronto, ON, Canada
| | - A teterina
- Toronto General Hospital, Toronto, ON, Canada
| | - E Comelli
- Department of Nutritional Sciences, Toronto, ON, Canada
| | - A Taibi
- Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - B Arendt
- Toronto General Hospital, Toronto, ON, Canada
| | - S Fischer
- Toronto General Hospital, Toronto, ON, Canada
| | - J Allard
- Toronto General Hospital, Toronto, ON, Canada
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Chelliah A, Jeelani R, Aguin T, Johnson S, Jain M. Rare Complication of Pelvic Radiation. J Minim Invasive Gynecol 2011. [DOI: 10.1016/j.jmig.2011.08.590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Loh LC, Chelliah A, Ang TH, Ali AM. Change in infection control practices and awareness of hospital medical staff in the aftermath of SARS. Med J Malaysia 2004; 59:659-64. [PMID: 15889569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Severe Acute Respiratory Syndrome (SARS) epidemic illustrated the crucial role of infection surveillance and control measures in the combat of any highly transmissible disease. We conducted an interview survey of 121 medical staff 145 doctors, 46 staff nurses and 30 medical assistants) in a state hospital in Malaysia three months after the end of SARS epidemic (from October to December 2003). Staff was grouped according to those directly involved in the care of suspected SARS patients [S+ group n=41] and those who were not [S- group; n=80]. On hand washing following sneezing, coughing and touching patients, the proportions of medical staff that reported an increase after the SARS crisis were 22.3%, 16.5% and 45.5% respectively. On wearing masks, gloves, and aprons when meeting potentially infectious patients, the proportions that reported an increase were 39.7%, 47.1% and 32.2% respectively. Significantly more staff in S+ than S- group reported these increases. Sixty percent of staff was aware of changes in hospital infection control policies after SARS; 93.4% was aware of notifying procedures, and 81.8% was aware of whom to notify in the hospital. Regarding infection isolation ward, Infectious Control Nurse and Infection Control Committee Chairman in the hospital, the proportions of staff that could correctly name them were 39.7%, 38.3% and 15.7% respectively. Significantly more in S+ than S- group could do so. However, more than half the staff claimed ignorance on the knowledge of infection isolation ward (56.2%), Infection Control Nurse (57.9%) and Chairman (65.3%). Our findings demonstrated that SARS crisis had some positive impact on the infection control practices and awareness of medical staff especially on those with direct SARS involvement. Implications for future control of infectious diseases are obvious.
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Affiliation(s)
- L C Loh
- Department of Medicine, Clinical School, International Medical University, Jalan Rasah, Seremban 70300, Negeri Sembilan
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Chelliah A, Burge MR. 155 THE MECHANISM OF ETHANOL INDUCED SUPPRESSION OF GROWTH HORMONE SECRETION IN DIABETES. J Investig Med 2004. [DOI: 10.1136/jim-52-suppl1-155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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