PROVIZ

Research projects

Developing AI-based software to identify prostate cancer in MR images.

Highlights 2023

The purpose of this project is to develop artificial intelligence (AI)-based software to support the detection and characterization of prostate cancer on MR images.

Screen-shot from the Proviz decision support tool showing the detection of a lesion in one of the men referred to the prospective proof of technology study

During 2023, our research focused on preparing the ground for conducting a prospective clinical proof of technology study to assess the feasibility, safety and performance of our decision support tool (Proviz). Clinical testing of medical device softwares are strictly regulated by the European Union Medical Device Regulation (2017/745), enforced by the Norwegian Medical Product Agency. This involves tedious paperwork, and the study was approved in April. Together with Hemit, we have set up an IT pipeline
directing MR images from the scanner through a server containing our algorithm, from which the tumor probability and detection maps are forwarded to the radiologist. A grant from NTNU Discovery was used to develop frontend software to visualize the results for the radiologist. The study will benchmark our decision support tool to clinical practice as preformed by a radiologist. The submitted patent is not granted yet, but the application is now publicly available (WO2023/214033). Importantly, our two Proviz PhD candidates completed their work, and are planning for their defenses.
One highlight of 2023 was to recruit the two first patients to the prospective clinical study. Recruitment is planned to be completed during the spring. We are very excited that our product is the first own-developed AI tool exploiting MR images in Norway to be tested in clinical flow.
For us, the added value of being part of a transdisciplinary center is the available expertise from life, data and social sciences, as well as the possibility to explore the relevance of this expertise through a direct link with the clinic. This is important to our project as the problems to be solved evolve from real-world clinical limitations, are of complex nature, and require out-of-the-box thinking.

Project overview

Project lead: Tone Frost Bathen
Institution: NTNU
Partners: St. Olav's Hospital and Norwegian University of Life Sciences (NMBU)
Duration: 2019–2022

Patsanis, Alexandros; Sunoqrot, Mohammed R. S.; Bathen, Tone Frost & Elschot, Mattijs (2023). CROPro: a tool for automated cropping of prostate magnetic resonance images. Journal of Medical Imaging. ISSN 2329-4302. 10(2), p. 1–19. doi: 10.1117/1.JMI.10.2.024004. Full text in Research Archive
Patsanis, Alexandros; Sunoqrot, Mohammed R. S.; Langørgen, Sverre; Wang, Hao; Selnæs, Kirsten Margrete & Bertilsson, Maria Helena [Show all 8 contributors for this article] (2023). A comparison of Generative Adversarial Networks for automated prostate cancer detection on T2-weighted MRI. Informatics in Medicine Unlocked (IMU). 39. doi: 10.1016/j.imu.2023.101234. Full text in Research Archive
Davik, Petter; Remmers, Sebastiaan; Elschot, Mattijs; Roobol, Monique J.; Bathen, Tone Frost & Bertilsson, Maria Helena (2023). Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres. BJU International. ISSN 1464-4096. doi: 10.1111/bju.16163.
Meglic, Jakob; Sunoqrot, Mohammed R. S.; Bathen, Tone Frost & Elschot, Mattijs (2023). Label-set impact on deep learning-based prostate segmentation on MRI. Insight into Imaging. ISSN 1869-4101. 14. doi: 10.1186/s13244-023-01502-w. Full text in Research Archive
Dewi, Dyah Ekashanti Octorina; Sunoqrot, Mohammed R. S.; Nketiah, Gabriel Addio; Sandsmark, Elise; Giskeødegård, Guro F. & Langørgen, Sverre [Show all 9 contributors for this article] (2023). The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features. Magnetic Resonance Materials in Physics, Biology and Medicine. ISSN 0968-5243. doi: 10.1007/s10334-023-01112-z.
Sørland, Kaia Ingerdatter; Sunoqrot, Mohammed R. S.; Sandsmark, Elise; Langørgen, Sverre; Bertilsson, Helena & Trimble, Christopher George [Show all 11 contributors for this article] (2022). Pseudo-T2 mapping for normalization of T2-weighted prostate MRI. Magnetic Resonance Materials in Physics, Biology and Medicine. ISSN 0968-5243. doi: 10.1007/s10334-022-01003-9. Full text in Research Archive
Davik, Petter; Remmers, Sebastiaan; Elschot, Mattijs; Roobol, Monique J; Bathen, Tone Frost & Bertilsson, Helena (2022). Reducing prostate biopsies and magnetic resonance imaging with prostate cancer risk stratification. BJUI Compass. ISSN 2688-4526. 3(5), p. 344–353. doi: 10.1002/bco2.146. Full text in Research Archive
Sunoqrot, Mohammed R. S.; Saha, Anindo; Hosseinzadeh, Matin; Elschot, Mattijs & Huisman, Henkjan J. (2022). Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges. European Radiology Experimental. 6:35, p. 1–13. doi: 10.1186/s41747-022-00288-8. Full text in Research Archive
Sunoqrot, Mohammed R. S.; Selnæs, Kirsten Margrete; Sandsmark, Elise; Langørgen, Sverre; Bertilsson, Helena & Bathen, Tone Frost [Show all 7 contributors for this article] (2021). The reproducibility of deep learning-based segmentation of the prostate gland and zones on t2-weighted mr images. Diagnostics (Basel). ISSN 2075-4418. 11(9), p. 1–15. doi: 10.3390/diagnostics11091690. Full text in Research Archive
Nketiah, Gabriel Addio; Elschot, Mattijs; Scheenen, Tom W.J.; Maas, Marnix C.; Bathen, Tone Frost & Selnæs, Kirsten Margrete (2021). Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study. Scientific Reports. ISSN 2045-2322. 11:2085, p. 1–13. doi: 10.1038/s41598-021-81272-x. Full text in Research Archive Show summary
T2-weighted (T2W) MRI provides high spatial resolution and tissue-specifc contrast, but it is predominantly used for qualitative evaluation of prostate anatomy and anomalies. This retrospective multicenter study evaluated the potential of T2W image-derived textural features for quantitative assessment of peripheral zone prostate cancer (PCa) aggressiveness. A standardized preoperative multiparametric MRI was performed on 87 PCa patients across 6 institutions. T2W intensity and apparent difusion coefcient (ADC) histogram, and T2W textural features were computed from tumor volumes annotated based on whole-mount histology. Spearman correlations were used to evaluate association between textural features and PCa grade groups (i.e. 1–5). Feature utility in diferentiating and classifying low-(grade group 1) vs. intermediate/high-(grade group≥ 2) aggressive cancers was evaluated using Mann–Whitney U-tests, and a support vector machine classifer employing “holdone-institution-out” cross-validation scheme, respectively. Textural features indicating image homogeneity and disorder/complexity correlated signifcantly (p< 0.05) with PCa grade groups. In the intermediate/high-aggressive cancers, textural homogeneity and disorder/complexity were signifcantly lower and higher, respectively, compared to the low-aggressive cancers. The mean classifcation accuracy across the centers was highest for the combined ADC and T2W intensitytextural features (84%) compared to ADC histogram (75%), T2W histogram (72%), T2W textural (72%) features alone or T2W histogram and texture (77%), T2W and ADC histogram (79%) combined. Texture analysis of T2W images provides quantitative information or features that are associated with peripheral zone PCa aggressiveness and can augment their lassifcation.
Syversen, Ingrid Framås; Elschot, Mattijs; Sandsmark, Elise; Bertilsson, Helena; Bathen, Tone Frost & Goa, Pål Erik (2021). Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate. PLOS ONE. ISSN 1932-6203. 16(5). doi: 10.1371/journal.pone.0252387. Full text in Research Archive Show summary
Background: Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment. Purpose: To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI). Material and methods: 62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm^2) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SF_slow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models. Results: All three models showed a significant difference (p<0.0001) between peripheral zone (PZ) tumor and normal tissue ROIs, but not between non-PZ tumor and BPH ROIs. The area under the receiver operating characteristics curve distinguishing tumor from prostate voxels was 0.956, 0.949 and 0.949 for the two-component, bi-exponential and mono-exponential models, respectively. The corresponding Spearman correlation coefficients between tumor values and Gleason Grade Group were fair (0.370, 0.499 and -0.490), but not significant. Conclusion: Signal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models.
Sunoqrot, Mohammed R. S.; Nketiah, Gabriel Addio; Selnæs, Kirsten Margrete; Bathen, Tone Frost & Elschot, Mattijs (2020). Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition. Magnetic Resonance Materials in Physics, Biology and Medicine. ISSN 0968-5243. 34(2), p. 309–321. doi: 10.1007/s10334-020-00871-3. Full text in Research Archive
Sunoqrot, Mohammed R. S.; Selnæs, Kirsten Margrete; Sandsmark, Elise; Nketiah, Gabriel Addio; Zavala-Romero, Olmo & Stoyanova, Radka [Show all 8 contributors for this article] (2020). A quality control system for automated prostate segmentation on T2-weighted MRI. Diagnostics (Basel). ISSN 2075-4418. 10(9), p. 1–16. doi: 10.3390/diagnostics10090714. Full text in Research Archive

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Bathen, Tone Frost (2023). Developing desicion support based on AI – from idea to clinical adoption .
Skolbekken, John-Arne (2023). Screening for kvinner, men ikke for menn?
Skolbekken, John-Arne (2023). Kan kunstig intelligens erstatte leger?
Meglic, Jakob; Sunoqrot, Mohammed R. S.; Bathen, Tone Frost & Elschot, Mattijs (2023). Impact of label-set on the performance of the deep learning-based segmentation of the prostate gland and zones on T2-weighted MR images.
Davik, Petter; Elschot, Mattijs; Bathen, Tone Frost; Remmers, Sebastiaan; Bertilsson, Helena & Roobol, Monique J. (2023). A0058 - Comparing all published MRI prostate cancer risk calculators in a large 2-centre European cohort, European Urology, European Urology. ISSN 0302-2838.
Solbjør, Marit; Lysø, Emilie Hybertsen ; Hesjedal, Maria Bårdsen & Skolbekken, John-Arne (2023). Norwegian men’s views on PSA screening for prostate cancer.
Patsanis, Alexandros; Sunoqrot, Mohammed R. S.; Langørgen, Sverre; Wang, Hao; Selnæs, Kirsten Margrete & Bertilsson, Helena [Show all 8 contributors for this article] (2022). A comparison of Generative Adversarial Networks for Automated Prostate Cancer Detection on T2-weighted Magnetic Resonance Images.
Andersen, Maria Karoline; Tessem, May-Britt; Debik, Julia; Abrahamsen, Bendik Skarre; Sørland, Kaia Ingerdatter & Giskeødegård, Guro F. [Show all 8 contributors for this article] (2022). Tumorteltet - På tokt etter kreftceller . Show summary
Vann er vesentlig i alt som lever, og helt essensielt i MR-teknologi. Da måler vi signalet fra vannet i kroppen og cellene våre, og får klare bilder av det som skjer under huden. Med MR-bilder og ‘tumorteltet’ vil vi vise hvordan vi ser forskjell på kreft og friske celler.
Sørland, Kaia Ingerdatter; Trimble, Christopher George; Sandsmark, Elise; Bathen, Tone Frost; Elschot, Mattijs & Cloos, Martijn A. (2022). Reducing streak artefacts in radial MR fingerprinting of the prostate through automated channel removal.
Davik, Petter (2022). Reducing Prostate MRIs and biopsies with risk stratification before and after MRI.
Tessem, May-Britt (2022). Tissue multi-omics of prostate cancer.
Dewi, Dyah Ekashanti Octorina; Sunoqrot, Mohammed R. S.; Nketiah, Gabriel Addio; Sandsmark, Elise; Langørgen, Sverre & Bertilsson, Maria Helena [Show all 8 contributors for this article] (2022). Developing a Delta Radiomics Framework for Prostate Cancer Progression Biomarkers in Patients under Active Surveillance: Pilot Study.
Saha, Anindo; Twilt, Jasper J.; Bosma, Joeran S.; van Ginneken, Bram; Yakar, Derya & Elschot, Mattijs [Show all 10 contributors for this article] (2022). Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge.
Remmers, Sebastiaan & Davik, Petter (2022). A comparison of the PLUM and RPCRC- MRI-based prostate cancer risk prediction models in two North American and European cohorts. Show summary
Introduction & Objectives: Multiparametric magnetic resonance imaging (mpMRI)-based risk calculators (RC) have emerged to augment traditional risk prostate cancer (PCa) prediction models. However, few data are available comparing performance of different mpMRI-based RCs in external cohorts or between different countries. We aimed to compare the Prospective Loyola University MRI Prostate Biopsy (PLUM) RC to the Rotterdam Prostate Cancer Risk Calculator (RPCRC)-MRI (fully CE marked) in men clinical suspicion of PCa in both an American and European center. Materials & Methods: Men who were biopsy-naïve or had a prior negative biopsy receiving MRI/transrectal ultrasound fusion-guided prostate biopsy with targeted and systematic cores at centers external to both models (UAB, NTNU) were included. Primary outcomes considered were diagnosis of any PCa (≥ grade group (GG) 1) and clinically significant (cs)PCa (≥GG2). Performance of the two models was evaluated by discrimination, calibration and decision curve analysis (DCA). Results: Of 674 patients, 209/371 (56.3%) and 127/371 (34.2%) at UAB and 239/303 (55%) and 125/303 (41%) at NTNU were diagnosed with any PCa and csPCa, respectively. Discrimination of the PLUM and RPCRC-MRI was similar for any PCa (84%, 95% CI 81-87 vs. 81%, 95% CI 77-84 respectively) and csPCa (87%, 95% CI 85-90 vs. 86%, 95% CI 84-89 respectively). Both the PLUM and RPCRC-MRI were well calibrated (Fig). The DCA showed as well comparable results between the RCs above the clinical relevant thresholds (Fig). The RPCRC-MRI could avoided, with a threshold of 20% for any PCa or 4% for csPCa, 18% of all biopsies, missing 2% of all csPCa. The PLUM could avoid, with a threshold of 15% for csPCa, 35% of all biopsies, missing 6% of all csPCa https://doi.org/10.1016/S0302-2838(22)00818-1
Bathen, Tone Frost (2022). Hva er klinisk forskning – og hvorfor bør kreftpasienter delta?
Bathen, Tone Frost (2022). My experiences in partnering on health-related EU grant applications .
Hesjedal, Maria Bårdsen (2022). Tverrfaglighet som argument for samlokalisering.
Lysø, Emilie Hybertsen & Hesjedal, Maria Bårdsen (2022). AI in prostate cancer diagnostics.
Bathen, Tone Frost & Solbjør, Marit (2022). Hva er klinisk forskning – og hvorfor bør kreftpasienter delta?
Solbjør, Marit (2022). Norwegian men’s views on PSA screening for prostate cancer.
Bathen, Tone Frost (2022). Metabolomics – a viable approach for increased understanding of breast cancer.
Bathen, Tone Frost (2022). ProstataCAG: Forbedret diagnostikk og behandling i pakkeforløpet for prostatakreft .
Lysø, Emilie Hybertsen ; Hesjedal, Maria Bårdsen; Skolbekken, John-Arne & Solbjør, Marit (2022). Norwegian men's views of AI in prostate cancer diagnostics.
Hesjedal, Maria Bårdsen (2022). Hvordan tilrettelegge for tverrfaglig forskningssamarbeid?
Hesjedal, Maria Bårdsen (2022). Artificial Intelligence in Prostate Cancer Diagnostics: Transdisciplinary Collaboration in a Norwegian Biotechnology Project.
Lysø, Emilie Hybertsen ; Hesjedal, Maria Bårdsen; Skolbekken, John-Arne & Solbjør, Marit (2022). Norwegian men's views of AI in prostate cancer diagnostics.
Sunoqrot, Mohammed R. S.; Selnæs, Kirsten Margrete; Abrahamsen, Bendik Skarre; Patsanis, Alexandros; Nketiah, Gabriel Addio & Bathen, Tone Frost [Show all 7 contributors for this article] (2022). A deep learning-based quality control system for co-registration of prostate MR images.
Patsanis, Alexandros (2021). Deep Learning and Feature extraction.
Patsanis, Alexandros; Sunoqrot, Mohammed R. S.; Sandsmark, Elise; Langørgen, Sverre; Bertilsson, Helena & Selnæs, Kirsten Margrete [Show all 10 contributors for this article] (2021). Improving Prostate Cancer Detection Using Bi-parametric MRI with Conditional Generative Adversarial Networks.
Sunoqrot, Mohammed R. S.; Selnæs, Kirsten Margrete; Abrahamsen, Bendik Skarre; Patsanis, Alexandros; Nketiah, Gabriel Addio & Bathen, Tone Frost [Show all 7 contributors for this article] (2021). A deep learning-based quality control system for co-registration of prostate MR images.
Sunoqrot, Mohammed R. S. (2021). Normalization, Segmentation, and Quality Control in Radiomics.
Sunoqrot, Mohammed R. S.; Selnæs, Kirsten Margrete; Sandsmark, Elise; Langørgen, Sverre; Bertilsson, Helena & Bathen, Tone Frost [Show all 7 contributors for this article] (2021). The repeatability of deep learning based segmentation of the prostate, peripheral and transition zones on T2-weighted MR images.
Bathen, Tone Frost (2021). MRI and artificial intelligence: Can we improve prostate cancer diagnostics?
Bathen, Tone Frost & Tessem, May-Britt (2021). Nye metoder og nye markører for sikrere diagnostisering og bedre behandling.
Bathen, Tone Frost & Nketiah, Gabriel Addio (2021). Editorial for "MRI Radiomics-Based Machine Learning for Predict of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions". JMRI. 54(5), p. 1474–1475. doi: 10.1002/jmri.27752..
Bathen, Tone Frost (2021). Kunstig intelligens i bildediagnostikk.
Bathen, Tone Frost & Ola, Kindseth (2021). Brukermedvirkning i forskning.
Dewi, Dyah Ekashanti Octorina (2021). Radiomics: An Introduction.
Dewi, Dyah Ekashanti Octorina; Sunoqrot, Mohammed R. S.; Nketiah, Gabriel Addio; Sandsmark, Elise; Langørgen, Sverre & Bertilsson, Helena [Show all 8 contributors for this article] (2021). Repeatability of Radiomic Features in T2-Weighted Prostate MRI: Impact of Pre-processing Configurations.
Elschot, Mattijs (2021). PET/MRI in Prostate Cancer: AI for improved diagnostics.
Davik, Petter (2021). Reduksjon av MR prostata og prostatabiopsier med risikostratifisering. Show summary
Reduksjon av MR prostata og prostatabiopsier ved risikostratifisering: validering av ERSPC risikokalkulatorer for norsk populasjon. Davik P a, b, Remmers S c, Elschot M d, e, Roobol M b, Bathen TF d, Bertilsson H a, b a. Institutt for klinisk og molekylær medisin (IKOM), NTNU, Trondheim; b. Urologisk avdeling, St Olavs Hospital, Trondheim; c. Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands; d. Institutt for sirkulasjon og bildediagnostikk (ISB), NTNU, Trondheim; e. Klinikk for bildediagnostikk, St Olavs Hospital, Trondheim. Bakgrunn Prostatabiopsi innebærer risiko for infeksjon samt for livstruende sepsis og død mens MR prostata er en kostbar og tidkrevende undersøkelse. Risiko-kalkulatorer basert på kliniske variabler kan predikere sannsynligheten for å finne prostatacancer ved biopsi og kan brukes til å utelate MR og/eller biopsi når sannsynligheten for cancer er lav. Kalkulatorene fra European Randomized Study of Screening for Prostate Cancer (ERSPC RC3/4) bruker Prostata Spesifikt Antigen (PSA), prostatavolum, funn fra Digital Rektal Eksaminasjon (DRE) og tidligere prostatabiopsihistorikk som uavhengige variabler i logistiske regresjonsmodeller. Disse gir sannsynligheten for klinisk signifikant cancer (csPCa) (Gleason ≥ 3+4) og all prostatakreft (PCa) og kan bidra til at 20 – 33% av biopsier unngås. Risikokalkulatorer som inkluderer MR-funn (bl.a. MRI-ERSPC-RC) bidrar til ytterligere reduksjon i biopsier, men krever MR-undersøkelse. Vi kalibrerte og re-kalibrerte ERSPC-RC 3/4 og MRI-ERSPC-RC til en norsk universitetssykehuskohort for å identifisere pasienter hvor MR og prostatabiopsier trygt kan utelates. Metode Alle menn som ble henvist med mistanke om PCa og gjennomgikk MR prostata med påfølgende prostatabiopsi mellom januar 2016 og mars 2017 ble retrospektivt identifisert og registrert i en database. Sannsynligheten for PCa og csPCa ble kalkulert med ukalibrerte kalkulatorer og sammenlignet med biopsifunn. RC-ene ble deretter rekalibrert til vår kohort og vurdert ved kalibrering, diskriminasjon med area under the curve (AUC) av receiver operating curves (ROC) og klinisk nytteverdi med Decision Curve Analyses (DCA). Resultat 303 menn ble inkludert. 79% var ikke tidligere biopsert. 55% hadde positive biopsier, 41% csPCa. AUC for ERSPC-RC 3/4 var 0.82 for csPCa og 0.77 for PCa. For MRI-ERSPC-RC var AUC 0.89 for csPCa of 0.85 for PCa. DCA viste klar nettogevinst for bruk av ERSPC RC 3/4 ved > 2 % terskelverdi for biopsi og for MRI-ERSPC-RC > 1 % terskelverdi. Ved > 10% sjanse for csPCa eller 20% sjanse for PCa terskelverdi ved bruk av ERSPC RC kunne 15.5% av MR prostata vært utelatt, 0.8% av csPCa ville da misses. Ved samme terskelverdi for bruk av MRI-ERSPC-RC kunne 24.4% av prostatabiopsier vært utelatt, 1.6% av csPCa ville da misses. Konklusjon Bruk av europeiske risikokalkulatorer rekalibrert til norske forhold kan gi betydelig reduksjon i antall MR prostata og prostatabiopsier med liten kostnad.
Patsanis, Alexandros; Sunoqrot, Mohammed R. S.; Sandsmark, Elise; Langørgen, Sverre; Bertilsson, Helena & Selnæs, Kirsten Margrete [Show all 9 contributors for this article] (2021). Prostate Cancer Detection on T2-weighted MR images with Generative Adversarial Networks.
Sørland, Kaia Ingerdatter; Goa, Pål Erik; Selnæs, Kirsten Margrete; Sandsmark, Elise; Trimble, Christopher George & Sunoqrot, Mohammed R. S. [Show all 8 contributors for this article] (2021). Pseudo-T2 mapping of T2-weighted MRI of the prostate: Comparison to gold standard.
Sørland, Kaia Ingerdatter; Sunoqrot, Mohammed R. S.; Goa, Pål Erik; Sandsmark, Elise; Langørgen, Sverre & Bertilsson, Helena [Show all 9 contributors for this article] (2021). Automated reference tissue normalization of prostate T2-weighted MRI on a large, multicenter dataset.
Nketiah, Gabriel Addio & Bathen, Tone Frost (2021). Editorial for “MRI Radiomics-Based Machine Learning for Predict of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions”. Journal of Magnetic Resonance Imaging. ISSN 1053-1807. 54(5), p. 1474–1475. doi: 10.1002/jmri.27752.
Solbjør, Marit; Bathen, Tone Frost; Brøgger, Helga Maria & Nydal, Rune (2020). KUNSTIG INTELLIGENS I KREFTDIAGNOSTIKK - MELLOM HÅP, REALISME OG ETISKE UTFORDRINGER. Show summary
Åpent møte på Litteraturhuset i Trondheim. Tre populærvitenskapelige foredrag samt paneldebatt. Foredragsholdere: Tone F. Bathen, NTNU: PROVIZ – et forskningsprosjekt om MR-visualisering av prostatakreft Helga Brøgger, Leder for Norsk radiologisk forening: Vidunderlig og vanskelig- AI i radiologi Rune Nydal, NTNU og NENT: Etiske utfordringer med AI i diagnostikk Møteleder: Marit Solbjør Møtet ble gjennomført med 10 publikum i salen og 55 publikum fulgte møtet digitalt pga covid-19-situasjonen.
Sunoqrot, Mohammed R. S.; Kucharczak, Sandra; Grajek, Magdalena; Selnæs, Kirsten Margrete; Bathen, Tone Frost & Elschot, Mattijs (2020). The repeatability of deep learning-based segmentation of the prostate on T2-weighted MR images.
Bathen, Tone Frost (2019). Prostate MR – improved diagnostics and treatment by use of artificial intelligence? The opportunities and challenges.
Bathen, Tone Frost (2019). Prostata MR: I hvilken grad kan teknologien hjelpe oss med å ta kloke valg?

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Research group

Project lead: Tone Frost Bathen

Professor

NTNU

Profile

1 in 8 men will have prostate cancer in their lives. PROVIZ is developing AI-based software to correctly identify prostate cancer in MR images, reducing the time and cost of diagnosis.

Norway was the first country to implement an integrated cancer care pathway that uses multi-parametric magnetic resonance imaging (mpMRI) as the first diagnostic tool for men with suspected prostate cancer based on elevated levels of prostate specific antigen (PSA) in their blood. However, the PSA blood test has a high false-positive rate, leading many men to get unnecessary mpMRI’s. The popularity of this blood test has created an excess of images for radiologists to analyze.

PROVIZ’s research will support radiologists by expediting the process of detecting cancer in the mpMRI images and by guiding biopsy targets. Today, radiologists rely on their training and experience to identify whether cancer is present and to guide biopsies to further test cancer aggressiveness. PROVIZ’s AI analyzes the quantitative information in mpMRI images (anatomy, vascularization, and cellularity) and builds 3D models to identify and visualize potential cancer risk, reducing the burden on radiologists and lowering biopsy risks.

The software PROVIZ is developing is based on a unique Norwegian dataset of MRI and clinical information from over 1600 patients with and without cancer, as well as collaboration with international teams collecting data in The Netherlands and Taiwan. This large data set allows the team to account for variation between clinical sites and create a truly universal diagnostic platform.

Clinicians, patients, and research participants have been involved in development from the start of the project to ensure the research aligns with stakeholder and social needs. This inclusive team has the background to anticipate both positive and negative outcomes of their work. In addition to experts in MRI technology, information analysis, radiology, oncology and communication between researchers and clinicians, the PROVIZ team includes a doctoral candidate specifically focused on ensuring that responsible research and innovation is integrated into the project.

Adaptable AI-based software has the potential to reduce health care costs, ease the burden on medical personnel, and obtain better treatment outcomes. Once the project is complete, PROVIZ aims to share their data set and invite others to use it to educate the field and develop additional tools.

PROVIZ is headed by Tone Bathen at the Norwegian University of Science and Technology in Trondheim. This lab is funded by the Research Council of Norway and is one of the multidisciplinary research projects within Centre for Digital Life Norway.

By Matthew Davidson

Latest news from the project

PROVIZ receives funding from Innovation Roadmap and NTNU Discovery! June 24, 2024 12:31 PM