Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer

Abstract

Histological Grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-lab variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model. The primary performance evaluation focuses on prognostic performance. This observational study is based on two patient cohorts (SöS-BC-4, N=2421 (training and internal test); SCAN-B-Lund, N=1262 (test)) that include routine histological whole slide images together with patient outcomes. A Deep Convolutional Neural Network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from hematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of Recurrence-free survival (RFS) and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. We observed effect sizes (Hazard Ratio) for grade 3 vs 1, for the conventional NHG method (HR=2.60 (1.18-5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI: 1.07-4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for NHG 1 vs 2 was estimated to be 2.59 (p-value = 0.004) and NHG 1 vs 3 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for grade 1 vs 2 HR=2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for grade 1 vs 3. In multivariable analysis, HR estimates for neither NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set, and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model provides similar prognostic performance as NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

Competing Interest Statement

JH has obtained speaker's honoraria or advisory board remunerations from Roche, Novartis, AstraZeneca, Eli Lilly and MSD and has received institutional research grants from Cepheid and Novartis. MR and JH are shareholders of Stratipath AB. All other authors have declared no conflicts of interest.

Funding Statement

This work was supported by funding from the Swedish Research Council, Swedish Cancer Society, Karolinska Institutet, ERA PerMed (ERAPERMED2019-224-ABCAP), MedTechLabs, Swedish e-science Research Centre (SeRC) - eCPC, Stockholm Region, Stockholm Cancer Society and Swedish Breast Cancer Association.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study has approval by the regional ethics review board (Stockholm, Sweden)

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

The data used in the present study is not publicly available

留言 (0)

沒有登入
gif