For breast cancer, the Ki-67 index gives important information on the patient’s prognosis and may predict the response to therapy. However, semi-automated methods for Ki-67 index calculation are prone to intra-and inter-observer variability, while fully automated machine learning models based on nuclei segmentation, classification and counting require training on large datasets with precise annotations down to the level of individual nuclei, which are hard to obtain. We design a neural network that straightforwardly predicts the Ki-67 index from scans of H&DAB-stained tissue samples. The network is trained only on existing data from daily operations at Masaryk Memorial Cancer Institute, Brno. The image labels contain only the Ki-67 index without any tumour epithelium or nuclei annotations. We use a simple convolutional neural network, not biasing the network by incorporation of layers dedicated to epithelium or nuclei segmentation or classification. Our model’s predictions align with the state-of-the-art evaluation by pathologists using QuPath image analysis with manual tumour annotation. On a test set consisting of 1250 images, the model achieved the mean absolute error of 3.668 and Pearson’s correlation coefficient of 0.959 (p < 0.001). Surprisingly, despite using a simple architecture and very weak supervision, the model persuasively detects complex morphological structures such as tumour epithelium. The model also works on Whole Slide Image data, e.g. to detect the hotspot areas. Since our approach does not need any specifically labelled data or additional staining, it is cost-effective and allows easy domain adaptation.

The authors have declared no competing interest.

This work has been supported by Czech Ministry of Health, (MMCI 00209805) and Czech Ministry of Education, Youth and Sports, (project BBMRI.cz, reg. no. LM2023033). AI infrastructure for the project was developed as a part of BioMedAI Center at Masaryk University, supported by the BioMedAI TWINNING project funded under EU Horizon Europe Programme, grant agreement no. 101079183.

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:

Institutional Review Board of the Masaryk Memorial Cancer Institute, Brno, Czech Republic, gave ethical approval for this work.

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, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

(email: 254905mail.muni.cz, fi.muni.cz, musilfi.muni.cz, xbrazdilfi.muni.cz}

(email:rudolf.nenutilmou.cz)

(email:hopetics.muni.cz)

This work has been supported by Czech Ministry of Health, (MMCI 00209805) and Czech Ministry of Education, Youth and Sports, (project BBMRI.cz, reg. no. LM2023033). AI infrastructure for the project was developed as a part of BioMedAI Center at Masaryk University, supported by the BioMedAI TWINNING project funded under EU Horizon Europe Programme, grant agreement no. 101079183.

All data produced in the present study are available upon reasonable request to the authors.