Sökning: WFRF:(Rantalainen Mattias)
> (2020-2024) >
Development and pro...
Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer
-
- Sharma, Abhinav (författare)
- Karolinska Institute
-
- Weitz, Philippe (författare)
- Karolinska Institute
-
- Wang, Yinxi (författare)
- Karolinska Institute
-
visa fler...
-
- Liu, Bojing (författare)
- Karolinska Institutet,Karolinska Institute,NYU Grossman School of Medicine
-
- Vallon-Christersson, Johan (författare)
- Lund University,Lunds universitet,Bröstcancer-genetik,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Breastcancer-genetics,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments
-
- Hartman, Johan (författare)
- Karolinska Institutet,Karolinska Institute,Karolinska University Hospital
-
- Rantalainen, Mattias (författare)
- Karolinska Institutet,Karolinska Institute,Karolinska University Hospital
-
visa färre...
-
(creator_code:org_t)
- 2024
- 2024
- Engelska.
-
Ingår i: Breast Cancer Research. - 1465-5411 .- 1465-542X. ; 26:1
- Relaterad länk:
-
http://dx.doi.org/10... (free)
-
visa fler...
-
https://lup.lub.lu.s...
-
https://doi.org/10.1...
-
http://kipublication...
-
visa färre...
Abstract
Ämnesord
Stäng
- Background: 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-laboratory 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 (predGrade). The primary performance evaluation focuses on prognostic performance. Methods: 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 (WSIs) 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 haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. Results: We observed effect sizes (hazard ratio) for grade 3 versus 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 clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical 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. Conclusion: 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 (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- Breast cancer
- Clinical decision support
- Deep learning
- Image analysis
- Pathology
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas