Tekoäly patologian kudosleikkeiden tulkinnassa

Vertaisarvioitu.English summary. Teema : tekoäly lääketieteessä.Konenäkö ja koneoppiminen ovat vauhdilla tulossa lääketieteeseen, erityisesti radiologiaan ja patologiaan. Hermoverkkojen avulla voidaan jo luokitella tautiryhmiä oikein sekä tunnistaa esimerkiksi syövän erilaistumisasteita. Konenäön odotetaan nopeuttavan lääkärin työtä ja vähentävän subjektiivisista arvioinneista johtuvaa vaihtelua diagnostiikassa, mikä johtaa laadukkaampaan diagnostiikkaan. Tekoäly ei korvaa lääkäriä, mutta oikein hyödynnettynä sille voidaan antaa rutiinitehtäviä ja vapauttaa näin luova ihmismieli vaikeampiin, parempaa kognitiota vaativiin tehtäviin sekä uuden kehittämiseen.Peer reviewe

Spectral decoupling for training transferable neural networks in medical imaging

Many neural networks for medical imaging generalize poorly to data unseen during training. Such behavior can be caused by overfitting easy-to-learn features while disregarding other potentially informative features. A recent implicit bias mitigation technique called spectral decoupling provably encourages neural networks to learn more features by regularizing the networks' unnormalized prediction scores with an L2 penalty. We show that spectral decoupling increases the networks' robustness for data distribution shifts and prevents overfitting on easy-to-learn features in medical images. To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer on tissue slides and COVID-19 in chest radiographs. Networks trained with spectral decoupling achieve up to 9.5 percent point higher performance on external datasets. Spectral decoupling alleviates generalization issues associated with neural networks and can be used to complement or replace computationally expensive explicit bias mitigation methods, such as stain normalization in histological images.Peer reviewe

Tekoälyä ja älytekoja : tekoälymenetelmät lääkärin päätöksenteon tukena

Vertaisarvioitu.Teema : tekoäly lääketieteessä

Spectral decoupling for training transferable neural networks in medical imaging

Many neural networks for medical imaging generalize poorly to data unseen during training. Such behavior can be caused by overfitting easy-to-learn features while disregarding other potentially informative features. A recent implicit bias mitigation technique called spectral decoupling provably encourages neural networks to learn more features by regularizing the networks' unnormalized prediction scores with an L2 penalty. We show that spectral decoupling increases the networks' robustness for data distribution shifts and prevents overfitting on easy-to-learn features in medical images. To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer on tissue slides and COVID-19 in chest radiographs. Networks trained with spectral decoupling achieve up to 9.5 percent point higher performance on external datasets. Spectral decoupling alleviates generalization issues associated with neural networks and can be used to complement or replace computationally expensive explicit bias mitigation methods, such as stain normalization in histological images.Peer reviewe

Active surveillance versus initial surgery in the long-term management of Bosniak IIF-IV cystic renal masses

There may be surgical overtreatment of complex cystic renal masses (CRM). Growing evidence supports active surveillance (AS) for the management for Bosniak IIF-III CRMs. We aimed to evaluate and compare oncological and pathological outcomes of Bosniak IIF-IV CRMs treated by initial surgery (IS) or AS. We identified retrospectively 532 patients with CRM counseled during 2006-2017. IS and AS were delivered to, respectively, 1 and 286 patients in Bosniak IIF, to 54 and 85 patients in III and to 85 and 21 patients in Bosniak IV. Median follow-up was 66 months (IQR 50-96). Metastatic progression occurred for 1 (0.3%) AS patient in Bosniak IIF, 1 IS (1.8%) and 1 AS (1.2%) patient in Bosniak III and 5 IS (3.5%) patients in Bosniak IV, respectively. Overall 5-year metastasis-free survival was 98.9% and cancer-specific survival was 99.6% without statistically significant difference between IS and AS in Bosniak IIF-IV categories. AS did not increase the risk of metastatic spread or cancer-specific mortality in patients with Bosniak IIF-IV. Our data indicate AS in Bosniak IIF and III is safe. Surgery is the primary treatment for Bosniak IV due to its high malignancy rate.Peer reviewe

AI Model for Prostate Biopsies Predicts Cancer Survival

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016–2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016–2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist’s diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist’s grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11–8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15–1.7; G5 OR: 5.58; 95% CI 0.45–3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2–1.8; G5 OR: 4.09; 95% CI 0.22–3). Algorithm-detected grade group 3–5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1–2 (HR: 5.91; 95% CI 1.96–17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment

AI Model for Prostate Biopsies Predicts Cancer Survival

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016–2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016–2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist’s diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist’s grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11–8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15–1.7; G5 OR: 5.58; 95% CI 0.45–3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2–1.8; G5 OR: 4.09; 95% CI 0.22–3). Algorithm-detected grade group 3–5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1–2 (HR: 5.91; 95% CI 1.96–17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment

Prognostic and predictive value of ALDH1, SOX2 and SSEA-4 in bladder cancer

Transurethral resection of bladder tumor (TUR-BT) and radical cystectomy (RC) are standard treatment options for bladder cancer (BC). Neoadjuvant chemotherapy (NAC) prior to RC improves outcome of some patients but currently there are no valid biomarkers to identify patients who benefit from NAC. Presence of cancer stem cells (CSC) has been associated with poor outcome and resistance to chemotherapy in various cancers. Here we studied the expression of stem cell markers ALDH1, SOX2 and SSEA-4 with immunohistochemistry in tissue microarray material consisting of 195 BC patients treated with RC and 74 patients treated with TUR-BT followed by NAC and RC. Post-operative follow-up data of up to 22 years was used. Negative to weak cytoplasmic SOX2 staining was associated with lymphovascular invasion and non-organ confined disease. It was also associated with shortened cancer-specific survival, but the finding was not statistically significant. Contrary to previous reports, none of the other tested biomarkers were associated with cancer-specific mortality or clinicopathological characteristics. Neither were they associated with response to NAC. Despite the promising results of previously published studies, our results suggest that CSC markers ALDH1, SOX2 and SSEA-4 have little if any prognostic or predictive value in BC treated with RC.Peer reviewe

Kasvaimen kolmiulotteinen histopatologia : malli kielen levyepiteelikarsinoomasta

Lääketieteessä kuvantamistutkimuksissa hyödynnetään usein kolmiulotteista (3D) mallintamista, jotta tutkittava kohde pystyttäisiin hahmottamaan yksiselitteisemmin. Sen sijaan histopatologiassa kaksiulotteinen (2D) esittämistapa on edelleen vallitseva tapa ilmoittaa esimerkiksi poistetun kasvaimen leikkausmarginaalit. Tutkimuksemme tarkoitus oli esittää leikkauksessa poistetun pehmytkudosresekaatin sisällä olevan kasvaimen dimensiot ja siitä tehtyjen histologisten leikkeiden sijainnit 3D muodossa luomalla resekaatista ja sen leikkeistä digitaalinen 3D-malli. Kehittelimme menetelmän käyttäen yleisesti saatavilla olevia instrumentteja keskittyen kielen levyepiteelikarsinooman mallintamiseen. Loimme menetelmän tunnistamalla ja ratkomalla ongelmia, jotka liittyivät histologisten leikkeiden leikesuuntien valintaan, joka aiemman kirjallisuuden perusteella on ollut keskeinen haaste pehmytkudosresekaatin 3D-mallin luomisessa. Tavanomaiseen resekaatin käsittelyyn verrattuna lisävaiheita olivat ainoastaan leikkausresekaatin skannaaminen ennen histopatologisten leikkeiden keräämistä sekä itse karsinooman mallintaminen digitaaliseksi. Nämä lisävaiheet vaativat ainoastaan 3D pöytäskannerin ja 3D mallinnusohjelmiston. Työssä esittelemme leikkausresekaatin ja histopatologisten leikkeiden mallintamiseen liittyviä haasteita ja niille kehittämiämme ratkaisuja. Työn tuloksena esittelemme valmiin 3D-mallin kielen levyepiteelikarsinooman leikkausresekaatista ja sen sisällä olevasta varsinaisesta kasvaimesta sekä digitaalisena mallina että puoliläpäisevänä valumallina (3D-tuloste). Kuvaamme työssä myös työvaiheet, jotka vaaditaan 3D-mallin luomiseksi. Julkaisuhetkellä tietääksemme työmme on ensimmäinen yritys esittää kielikasvaimen histopatologiset marginaalit 3D muodossa, kun aiemmin vain 2D muoto on ollut saatavilla. 3D-mallin luominen metodillamme ei vaadi ennalta määrättyjä leikesuuntia. Metodimme tarjoaa yksiselitteisemmän ja selkeämmän tavan havainnollistaa kasvaimen marginaalit, topografia ja orientaatio. Metodiamme voitaisiin tulevaisuudessa käyttää työkaluna postoperatiivisessa arvioinnissa sekä adjuvanttihoitojen suunnitelussa