“Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction” was accepted by Nature by technology on February 22.
https://www.nature.com/articles/s41467-023-36202-y
Hartman together with Rantalainen and Wetterhall recieved the Athena award for Stratipath.
The Athena Award 2022 was awarded on October 25th to Stratipath, an AI-based decision support system for prognostic analysis of breast cancer directly from microscope images. The purpose of the Athena Award is to reward and recognize research that has been carried out in collaboration between healthcare, academia, and industry. Stratipath meets the criteria for the award very well and is an exciting example of how artificial intelligence can be used in medicine to provide equitable access to diagnosis and treatment.
The largest newspaper in sweden features our ongoing research in precision pathology
https://www.dn.se/sverige/ny-metod-hittar-ratt-behandling-for-brostcancerpatienter/
Publication in PNAS
“Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction” was accepted by PNAS on November 7.
https://www.pnas.org/doi/10.1073/pnas.2209856120
Hartman and Rantalainen joint publication in Annals of Oncology
Impact factor 33.
AI improves precision in breast cancer diagnosis
Press release: https://news.ki.se/ai-improves-precision-in-breast-cancer-diagnosis
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Researchers at Karolinska Institutet have developed an AI-based tool that improves the diagnosis of breast cancer tumours and the ability to predict the risk of recurrence. The greater diagnostic precision can lead to more personalised treatment for the large group of breast cancer patients with intermediate risk tumours. The results are published in the scientific journal Annals of Oncology.
Every year, around two million women globally develop breast cancer. In the diagnostic procedure, tissue samples of the tumour are analysed and graded by a pathologist and categorized by risk as low (grade 1), medium (grade 2) or high (grade 3). This helps the doctor determine the most suitable treatment for the patient.
Gives no clear guidance
“Roughly half of breast cancer patients have a grade 2 tumour, which unfortunately gives no clear guidance on how the patient is to be treated,” says the study’s first author Yinxi Wang, doctoral student at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet. “Consequently, some of the patients are over-treated with chemotherapy while others risk being under-treated. It’s this problem that we’ve tried to resolve.”
Hospitals have recently started to make limited use of molecular diagnostics to improve the precision of breast cancer risk assessment, but these methods are often costly and time-consuming. The researchers at Karolinska Institutet have now developed and evaluated an AI (artificial intelligence)-based method for tissue analysis. The study shows that the AI-based method can further divide the patients with grade 2 tumours into two sub-groups, one high-risk and one low-risk, that are clearly distinguishable in terms of the recurrence risk.
Fast and cost-effective method
“One big advantage of the method is that it’s cost-effective and fast, since it’s based on microscope images of dyed tissue samples, which is already part of hospital procedure,” says co-last author Johan Hartman, professor of pathology at the Department of Oncology-Pathology, Karolinska Institutet, and pathologist at the Karolinska University Hospital. “It enables us to offer this type of diagnosis to more people and improves our ability to give the right treatment to any one patient.”
The AI model has been trained to recognise characteristics of high-resolution microscopic images from patients classified with grade 1 and grade 3 tumours. The study is based on an extensive microscopic image bank of 2,800 tumours.
“It’s fantastic that deep learning can help us develop models that don’t just reproduce what specialist doctors do today, but also enable us to extract information beyond the scope of the human eye,” says co-last author Mattias Rantalainen, associate professor and research group leader at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet.
Could soon be on the market
The method is not yet ready for clinical application, but a regulatorily approved product is under development by a newly started company, Stratipath AB, which is supported by KI Innovations. The researchers will now be further evaluating the method with the aim to have a product out on the market by 2022.
The study was financed by the Swedish Research Council, the Swedish Cancer Society, Karolinska Institutet, ERA PerMed, the European Research Council, MedTechLabs, Swedish e-science Research Centre (SeRC), Region Stockholm, the Cancer Society in Stockholm and the Swedish Breast Cancer Association.
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Publication, Annals of Oncology: “Improved breast cancer histological grading using deep learning”. Y. Wang, B. Acs, S. Robertson, B. Liu, L. Solorzano, C. Wählby, J. Hartman, M. Rantalainen. Annals of Oncology, online 30 September 2021, doi: 10.1016/j.annonc.2021.09.007.
Balazs Acs received a 3-year postdoc grant from SSMF
Balazs Acs received a 3-year postdoc grant from the Swedish Society for Medical Research.
https://www.ssmf.se/for-forskare/ssmfs-postdoktorala-studier/ssmfs-postdoktorala-anslag_in-english/
MedTechLabs ska utveckla banbrytande behandling och diagnos genom AI och bioelektronisk medicin
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Two new research programmes focusing on AI and bioelectronic medicine, for application in the areas of breast cancer and inflammatory disease, respectively, have been adopted at MedTechLabs. The research is expected to start in January 2020.
In June 2019, MedTechLabs opened a call for a second research area within the centre. The newly appointed Centre Director Peta Sjölander says that both programmes live up to MedTechLab’s focus:
“Research at the centre should be able to achieve breakthroughs in the respective areas and provide results that can benefit healthcare already within five years. The programmes are therefore based on established research.”
The first new programme uses AI and machine learning to radically increase the accuracy of breast cancer imaging diagnostics. Associate Professor and clinician Johan Hartman, researcher at KI, and Associate Professor Kevin Smith, researcher at KTH and SciLifeLab, will lead this programme, which also involves other researchers from KI and KTH. The programme will use decoded data from all patients diagnosed with breast cancer through mammography in the Stockholm region during the period 2005 and 2019.
“This important research programme is possible only through Sweden’s unique access to comprehensive and quality-assured patient data”, says Peta Sjölander.
Every year, approximately 1 500 women die from breast cancer in Sweden, and more and more cases are being detected. At the same time, relative mortality from the disease has decreased. Peta Sjölander believes that the research programme will contribute to a faster and better diagnosis and thus the opportunity to cure more patients and detect cancer earlier throughout the course of the disease.
The second new programme aims for the monitoring and stimulation of the vagus nerve with short electrical pulses, known as bioelectronic medicine, in order to treat inflammatory conditions. The programme is run by Associate Professor Peder Olofsson, researcher at KI and Henrik Hult, Professor at KTH, together with additional researchers from their universities. The programme will also employ doctoral students and researchers at the beginning of their careers.
“To our knowledge, the programme is the first in Europe to implement bioelectronic medicine clinically for the treatment of inflammatory disease in a patient-friendly environment,” says Peta Sjölander.
All research programmes at MedTechLabs are jointly led by a researcher from KTH and one from KI. The operations are mainly conducted in the new research building at the Karolinska University Hospital, BioClinicum in Solna, which has access to advanced medical emergency care, a prerequisite when applying techniques and treatment to patients.
Both programmes are expected to start in January 2020.
MedTechLabs is an interdisciplinary centre for patient-centered research that will contribute to the development of medical technology that is important for the challenges of healthcare. The centre is run by KTH, Karolinska Institutet and the Stockholm Region.
For more information, please contact:
Peta Sjölander, peta.sjolander@medtechlabs.se, 070-771 48 80
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