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DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence

Publication ,  Conference
Hagos, YB; Sobhani, F; Castillo, SP; Hall, AH; AbdulJabbar, K; Salgado, R; Harmon, B; Gallagher, K; Kilgore, M; King, LM; Marks, JR; Maley, C ...
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2022

Tumour infiltrating lymphocytes (TIL) influence the prognosis of Ductal carcinoma in situ (DCIS). Currently, manual assessment of TIL by expert pathologists is considered a gold standard. However, there are issues with a shortage of expert pathologists and inter-observer variability. A reliable automated scoring method is yet to be developed due to the inherent complexity of DCIS duct morphology and the assessment strategy. We developed a new deep learning and spatial analysis pipeline to automatically score DCIS stromal TIL (AI-TIL) from 243 diagnostic haematoxylin and eosin-stained whole slide images from 127 patients. To automatically identify and segment DCIS ducts, we implemented a generative adversarial network. To identify lymphocytes, we used a pre-trained deep learning model. Our DCIS segmentation model achieved a dice overlap of 0.94 (± 0.01 ) and the cell classifier model achieved 92% accuracy compared to pathologists’ annotations. Subsequently, we automatically delineated a stromal boundary and computed the percentage of the boundary area occupied by lymphocytes for each DCIS duct. Finally, we computed TIL score as the average of all duct level scores within the slide. We observe a higher correlation between AI-TIL and pathologists (average) score for wider stomal boundaries (r = 0.66, p = 6.0 × 10 - 7, W = 0.3 mm) compared with smaller boundary (r = 0.23, p = 0.12, W = 0.03 mm). Using multivariate analysis, a low AI-TIL score was associated with an increased risk of recurrence independent of age, grade, estrogen receptor (ER) status, progesterone receptor (PR) status, and necrosis (hazard ratio = 0.14, 95% CI 0.038–0.51, p = 0.003, W = 0.03 mm). These results suggest that our pipeline could be used to automatically quantify stromal TIL in DCIS and integrating AI-TIL with pathologists’ visual assessment may improve DCIS recurrence risk estimation.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783031196591

Publication Date

January 1, 2022

Volume

13602 LNCS

Start / End Page

164 / 175

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Hagos, Y. B., Sobhani, F., Castillo, S. P., Hall, A. H., AbdulJabbar, K., Salgado, R., … Yuan, Y. (2022). DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13602 LNCS, pp. 164–175). https://doi.org/10.1007/978-3-031-19660-7_16
Hagos, Y. B., F. Sobhani, S. P. Castillo, A. H. Hall, K. AbdulJabbar, R. Salgado, B. Harmon, et al. “DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13602 LNCS:164–75, 2022. https://doi.org/10.1007/978-3-031-19660-7_16.
Hagos YB, Sobhani F, Castillo SP, Hall AH, AbdulJabbar K, Salgado R, et al. DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 164–75.
Hagos, Y. B., et al. “DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13602 LNCS, 2022, pp. 164–75. Scopus, doi:10.1007/978-3-031-19660-7_16.
Hagos YB, Sobhani F, Castillo SP, Hall AH, AbdulJabbar K, Salgado R, Harmon B, Gallagher K, Kilgore M, King LM, Marks JR, Maley C, Horlings HM, West R, Hwang ES, Yuan Y. DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 164–175.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783031196591

Publication Date

January 1, 2022

Volume

13602 LNCS

Start / End Page

164 / 175

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences