An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data.

Artificial Intelligence Biomarkers Humans Male Middle Aged Neoplasms / diagnostic imaging Retrospective Studies Tomography, X-Ray Computed / methods

Antiangiogenic treatment Artificial intelligence Biomarker Imaging Prognosis Solid tumour

Journal

European journal of cancer (Oxford, England : 1990)

ISSN: 1879-0852

Titre abrégé: Eur J Cancer

Pays: England

ID NLM: 9005373

Informations de publication

Date de publication:
10 2022

Historique:

received: 24 05 2022

revised: 27 06 2022

accepted: 28 06 2022

pubmed: 20 8 2022

medline: 21 9 2022

entrez: 19 8 2022

Statut: ppublish

Résumé

The need for developing new biomarkers is increasing with the emergence of many targeted therapies. Artificial Intelligence (AI) algorithms have shown great promise in the medical imaging field to build predictive models. We developed a prognostic model for solid tumour patients using AI on multimodal data. Our retrospective study included examinations of patients with seven different cancer types performed between 2003 and 2017 in 17 different hospitals. Radiologists annotated all metastases on baseline computed tomography (CT) and ultrasound (US) images. Imaging features were extracted using AI models and used along with the patients' and treatments' metadata. A Cox regression was fitted to predict prognosis. Performance was assessed on a left-out test set with 1000 bootstraps. The model was built on 436 patients and tested on 196 patients (mean age 59, IQR: 51-6, 411 men out of 616 patients). On the whole, 1147 US images were annotated with lesions delineation, and 632 thorax-abdomen-pelvis CTs (total of 301,975 slices) were fully annotated with a total of 9516 lesions. The developed model reaches an average concordance index of 0.71 (0.67-0.76, 95% CI). Using the median predicted risk as a threshold value, the model is able to significantly (log-rank test P value < 0.001) isolate high-risk patients from low-risk patients (respective median OS of 11 and 31 months) with a hazard ratio of 3.5 (2.4-5.2, 95% CI). AI was able to extract prognostic features from imaging data, and along with clinical data, allows an accurate stratification of patients' prognoses.

Sections du résumé

BACKGROUND

PATIENTS AND METHODS

Our retrospective study included examinations of patients with seven different cancer types performed between 2003 and 2017 in 17 different hospitals. Radiologists annotated all metastases on baseline computed tomography (CT) and ultrasound (US) images. Imaging features were extracted using AI models and used along with the patients' and treatments' metadata. A Cox regression was fitted to predict prognosis. Performance was assessed on a left-out test set with 1000 bootstraps.

RESULTS

The model was built on 436 patients and tested on 196 patients (mean age 59, IQR: 51-6, 411 men out of 616 patients). On the whole, 1147 US images were annotated with lesions delineation, and 632 thorax-abdomen-pelvis CTs (total of 301,975 slices) were fully annotated with a total of 9516 lesions. The developed model reaches an average concordance index of 0.71 (0.67-0.76, 95% CI). Using the median predicted risk as a threshold value, the model is able to significantly (log-rank test P value < 0.001) isolate high-risk patients from low-risk patients (respective median OS of 11 and 31 months) with a hazard ratio of 3.5 (2.4-5.2, 95% CI).

CONCLUSION

AI was able to extract prognostic features from imaging data, and along with clinical data, allows an accurate stratification of patients' prognoses.

Identifiants

DOI: 10.1016/j.ejca.2022.06.055 PMID: 35985252

pubmed: 35985252

pii: S0959-8049(22)00407-5

doi: 10.1016/j.ejca.2022.06.055

pii:

doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

Pagination

90-98