Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs

Bonnie T Chao; Andrew T Sage; Micheal C McInnis; Jun Ma; Micah Grubert Van Iderstine; Xuanzi Zhou; Jerome Valero; Marcelo Cypel; Mingyao Liu; Bo Wang; Shaf Keshavjee

doi:10.1038/s41746-024-01260-z

Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs

NPJ Digit Med. 2024 Oct 3;7(1):272. doi: 10.1038/s41746-024-01260-z.

Authors

Bonnie T Chao^{1

2}, Andrew T Sage^{1

3

4}, Micheal C McInnis^{5

6}, Jun Ma^{7

8

9}, Micah Grubert Van Iderstine¹, Xuanzi Zhou^{1

3}, Jerome Valero¹, Marcelo Cypel^{1

3

4}, Mingyao Liu^{1

3

4}, Bo Wang^{7

8

9

10

11}, Shaf Keshavjee^{12

13

14

15

16}

Affiliations

¹ Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
² Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
³ Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
⁴ Department of Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
⁵ University Medical Imaging Toronto, Toronto General Hospital, University Health Network, Toronto, ON, Canada.
⁶ Department of Medical Imaging, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
⁷ Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada.
⁸ Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
⁹ Vector Institute, University of Toronto, Toronto, ON, Canada.
¹⁰ AI Hub, University Health Network, Toronto, ON, Canada.
¹¹ Department of Computer Science, University of Toronto, Toronto, ON, Canada.
¹² Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada. [email protected].
¹³ Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada. [email protected].
¹⁴ Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. [email protected].
¹⁵ Department of Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. [email protected].
¹⁶ AI Hub, University Health Network, Toronto, ON, Canada. [email protected].

Abstract

Ex vivo lung perfusion (EVLP) enables advanced assessment of human lungs for transplant suitability. We developed a convolutional neural network (CNN)-based approach to analyze the largest cohort of isolated lung radiographs to date. CNNs were trained to process 1300 longitudinal radiographs from n = 650 clinical EVLP cases. Latent features were transformed into principal components (PC) and correlated with known radiographic findings. PCs were combined with physiological data to classify clinical outcomes: (1) recipient time to extubation of <72 h, (2) ≥ 72 h, and (3) lungs unsuitable for transplantation. The top PC was significantly correlated with infiltration (Spearman R: 0·72, p < 0·0001), and adding radiographic PCs significantly improved the discrimination for clinical outcomes (Accuracy: 73 vs 78%, p = 0·014). CNN-derived radiographic lung features therefore add substantial value to the current assessments. This approach can be adopted by EVLP centers worldwide to harness radiographic information without requiring real-time radiological expertise.