Dataset Information

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.

ABSTRACT:

Background

We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer.

Methods

A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3⁺ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival.

Result

Patients were classified into 4-level score groups (score 1-4). A high Deep-immune score was associated with a high level of CD3⁺ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15-0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15-0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018).

Conclusion

The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine.

SUBMITTER: Yang J

PROVIDER: S-EPMC9533523 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.

Yang Jing J Ye Huifen H Fan Xinjuan X Li Yajun Y Wu Xiaomei X Zhao Minning M Hu Qingru Q Ye Yunrui Y Wu Lin L Li Zhenhui Z Zhang Xueli X Liang Changhong C Wang Yingyi Y Xu Yao Y Li Qian Q Yao Su S You Dingyun D Zhao Ke K Liu Zaiyi Z

Journal of translational medicine 20221004 1

<h4>Background</h4>We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer.<h4>Methods</h4>A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor ...[more]

PMID: 36195956

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Project description:SignificanceQuantifying meibomian gland morphology from meibography images is used for the diagnosis, treatment, and management of meibomian gland dysfunction in clinics. A novel and automated method is described for quantifying meibomian gland morphology from meibography images.PurposeMeibomian gland morphological abnormality is a common clinical sign of meibomian gland dysfunction, yet there exist no automated methods that provide standard quantifications of morphological features for individual glands. This study introduces an automated artificial intelligence approach to segmenting individual meibomian gland regions in infrared meibography images and analyzing their morphological features.MethodsA total of 1443 meibography images were collected and annotated. The dataset was then divided into development and evaluation sets. The development set was used to train and tune deep learning models for segmenting glands and identifying ghost glands from images, whereas the evaluation set was used to evaluate the performance of the model. The gland segmentations were further used to analyze individual gland features, including gland local contrast, length, width, and tortuosity.ResultsA total of 1039 meibography images (including 486 upper and 553 lower eyelids) were used for training and tuning the deep learning model, whereas the remaining 404 images (including 203 upper and 201 lower eyelids) were used for evaluations. The algorithm on average achieved 63% mean intersection over union in segmenting glands, and 84.4% sensitivity and 71.7% specificity in identifying ghost glands. Morphological features of each gland were also fed to a support vector machine for analyzing their associations with ghost glands. Analysis of model coefficients indicated that low gland local contrast was the primary indicator for ghost glands.ConclusionsThe proposed approach can automatically segment individual meibomian glands in infrared meibography images, identify ghost glands, and quantitatively analyze gland morphological features.

Dataset Information

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.

Background

Methods

Result

Conclusion

Publications

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.

Similar Datasets

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