A deep learning–based algorithm for tall cell detection in papillary thyroid carcinoma

Introduction According to the World Health Organization, the tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC) comprising at least 30% epithelial cells two to three times as tall as they are wide. In practice, applying this definition is difficult causing substantial interobserver variability. We aimed to train a deep learning algorithm to detect and quantify the proportion of tall cells (TCs) in PTC. Methods We trained the deep learning algorithm using supervised learning, testing it on an independent dataset, and further validating it on an independent set of 90 PTC samples from patients treated at the Hospital District of Helsinki and Uusimaa between 2003 and 2013. We compared the algorithm-based TC percentage to the independent scoring by a human investigator and how those scorings associated with disease outcomes. Additionally, we assessed the TC score in 71 local and distant tumor relapse samples from patients with aggressive disease. Results In the test set, the deep learning algorithm detected TCs with a sensitivity of 93.7% and a specificity of 94.5%, whereas the sensitivity fell to 90.9% and specificity to 94.1% for non-TC areas. In the validation set, the deep learning algorithm TC scores correlated with a diminished relapse-free survival using cutoff points of 10% (p = 0.044), 20% (p < 0.01), and 30% (p = 0.036). The visually assessed TC score did not statistically significantly predict survival at any of the analyzed cutoff points. We observed no statistically significant difference in the TC score between primary tumors and relapse tumors determined by the deep learning algorithm or visually. Conclusions We present a novel deep learning–based algorithm to detect tall cells, showing that a high deep learning–based TC score represents a statistically significant predictor of less favorable relapse-free survival in PTC.Peer reviewe

A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

Background Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites. Methods Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4′,6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears. Results Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p <0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p <0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples. Conclusion Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.Peer reviewe

An Overview of the Scala Programming Language

Scala fuses object-oriented and functional programming in a statically typed programming language. It is aimed at the construction of components and component systems. This paper gives an overview of the Scala language for readers who are familar with programming methods and programming language design

Interaction between unmanned vessels and COLREGs

Autonomous or remote controlled vessels are a completely new subject, which are under development right now. There is a possibility that in a few years we will see unmanned vessels sailing in international waters. These vessels will still have to be following the navigational rules at sea. This thesis examines unmanned vessels and how they will interact with the current amendment of the International Regulations for Preventing Collisions at Sea (COLREGS), mainly part A and B. The purpose of this thesis is to investigate how unmanned vessels interact with the navigational rules in COLREGS and if there will be any obstacles for implementing them in international waters. As research methodology we used a literature-based theoretical analysis in combination with a qualitative research. Our conclusions are that compliance with COLREGS doesn’t seem to be a huge problem for unmanned vessels. Most experts agree that compliance with COLREGS isn’t a serious issue and the liability in case something happens is a more serious problem. Unmanned vessels have already been tested in a few projects where they have been able to follow the Collision Regulations and navigate safely without accidents. However, a few things such as look-out and responsibility might need clarification before unmanned vessels will start to appear on a larger scale

The prognostic significance of tall cells and lymphocytes in papillary thyroid carcinoma : the use of deep learning algorithms

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer variant with an overall excellent prognosis. However, some histological subtypes demonstrate a more aggressive disease progression and thus require more attention from clinicians. The tall cell subtype of papillary thyroid carcinoma (TC-PTC) is one such subtype; it is characterized by the presence of tall epithelial cells, comprising approximately 30% of the tumor volume. These cells are three times taller than they are wide, and display nuclear features consistent with classical PTC. This definition, however, is challenging to adhere to by traditional microscopy, which results in large inter-observer variability between pathologists when diagnosing TC-PTC. The aim of this thesis was to study the tall cell (TC) threshold needed for an adverse outcome. We trained a deep learning (DL)-based algorithm for tall cell detection and scoring. The TC-algorithm detected TC areas with a sensitivity and specificity of 93.7% and 94.5%, respectively, and non-TC areas with a sensitivity and specificity of 90.3% and 94.1%, respectively, in the test set. The performance of the TC-algorithm was compared to visual TC scoring on an internal validation dataset. A higher TC score assessed by the TC-algorithm correlated with a reduced relapse-free survival (RFS) for 10%, 20%, and 30% TC thresholds. The visually assessed TC scores did not predict survival at any of the analyzed TC thresholds. The trained TC-algorithm was further externally validated using held-out multicenter PTC datasets, one originating from Auria Biobank, Turku, Finland, and the other from the University of Bern, Switzerland. In the external validation, the DL-based algorithm detected TC areas with a sensitivity and specificity of 90.6% and 88.5%, respectively, while the non-TC areas were detected with a sensitivity and specificity of 81.6% and 92.9%, respectively. In the external validation datasets, a higher TC score correlated with a reduction in relapse-free survival using a 20% and 30% TC threshold. Immune cells of the tumor microenvironment play an important role in the development and progression of cancers and may have either tumor promoting or suppressing effects. High numbers of tumor-infiltrating lymphocytes (TILs) have been associated with a favorable outcome in certain cancers such as breast and colon cancer, and have also been shown to correlate with a favorable prognosis in PTC. Quantifying TILs is routinely performed by visual evaluation and estimation using a traditional microscope, which is time-consuming and subject to inter- and intra-observer variability. In this thesis, we also trained a DL-based algorithm for segmenting TIL areas in PTC. We trained this model using a novel antibody-supervised learning approach with a pan-leukocyte CD45 antibody staining as ground truth, and applied the model to hematoxylin and eosin (HE)-stained tissue slides. Twelve PTC whole slide images (WSIs) were analyzed by the trained algorithm, which had an intersection over union of 0.82 for detecting TIL areas in HE-stained tissue slides when comparing the algorithm predictions to the ground truth anti-CD45 mask. Conclusively, the findings suggest that a DL-based algorithm approach can register and find TCs with high sensitivity and specificity, even in externally collected, independent datasets without any supportive training. An algorithm TC threshold of 30% correlated with a reduction in relapse-free survival, and is suggested to be used when diagnosing TC-PTC. All cases with a TC score above 10%, i.e., PTC with tall cell features, should be reported by the pathologist. We also conclude that the proposed method of generating antibody-supervised annotations using destain-restain immunohistochemistry-guided annotations resulted in highly accurate segmentation of TIL-rich areas in HE-stained tissue images.Papillaarinen karsinooma (PTC) on yleisin kilpirauhassyöpä. Sen ennuste on yleensä erinomainen. Jotkut PTC:n histologiset alatyypit ovat kuitenkin aggressiivisempia ja vaativat siksi erilaista hoitoa ja seurantaa. Korkeasoluinen alatyyppi (Tall cell-alatyyppi = TC-PTC) on tavallista PTC: tä agressiivisempi muoto. Määritelmän mukaan vähintään 30 % kasvainsoluista on kolme kertaa niin pitkiä kuin leveitä, mutta muut tumapiirteet kuten uurteet ja vakuolit vastaavat perinteistä PTC:n morfologiaa. Mikroskoopilla diagnosoitaessa TC-PTC:n piirteiden tarkka kvantitoiminen on epäluotettavaa, ja tämä johtaa merkittäviin patologien välisiin eroihin korkeasolujen määrää arvioitaessa. Tämän väitöskirjan tavoitteena oli tutkia korkeasolujen raja-arvon korrelaatiota potilaiden ennusteeseen kilpirauhassyövässä. Koulutimme syväoppimiseen (DL) perustuvan algoritmin korkeasolujen havaitsemiseen ja määrän arviointiin. TC-algoritmi tunnisti testisarjassa TC-alueet 93,7 % herkkyydellä ja 94,5 % tarkkuudella sekä ei-TC-alueet 90,3 % herkkyydellä ja 94,1 % tarkkuudella. TC-algoritmin tuloksia verrattiin visuaalisesti arvioituun TC-osuuteen sisäisessä validaatioaineistossa. TC-algoritmin antama TC-arvo korreloi lyhyempään uusiutumisvapaaseen selviytymiseen (RFS) 10 %, 20 % ja 30 %:n TC-kynnyksillä. Visuaalisesti arvioidut TC-osuudet eivät ennustaneet selviytymistä millään analysoiduista TC-kynnyksistä. Koulutettu TC-algoritmi validoitiin myös ulkopuolisilla PTC-aineistoilla, jotka olivat Auria Biopankista, Turusta sekä Bernin yliopistosta, Sveitsistä. Ulkoisessa validoinnissa DL-pohjainen algoritmi tunnisti TC-alueet 90,6 % herkkyydellä ja 88,5% tarkkuudella sekä ei-TC-alueet 81,6 % herkkyydellä ja 92,9 % tarkkuudella. Validointiaineistossa suuri TC-arvo korreloi epäsuotuisan RFS:n kanssa käytettäessä 20 % ja 30 %:n TC-kynnystä. Kasvaimen mikroympäristön immuunisoluilla on tärkeä rooli syövän kehityksessä ja etenemisessä ja ne voivat vaikuttaa joko kasvua edistävästi tai hillitsevästi. Lisääntynyt kasvaimen sisäisten lymfosyyttien (TILs) määrä on liitetty parempaan ennusteeseen esimerkiksi rinta- ja paksusuolisyövissä, mutta myös papillaarisessa kilpirauhaskarsinoomassa. TIL:ien määrän arvioiminen visuaalisesti mikroskoopilla on vastavasti epäluotettavaa ja väitöskirjan yhden osatyön tavoitteena oli kouluttaa syväoppimiseen perustava algoritmi TIL-alueiden segmentointiin PTC:ssä. Algoritmi perustui CD45-immunohistokemian (pan-leukosyytti) hyödyntämiseen TIL:ien opettamiseksi algoritmille hematoksyliini-eosiini-värjätyistä PTC-laseista. Kaksitoista PTC-tapausta analysoitiin koulutetulla TIL-algoritmilla. Algoritmin ”intersection over union” (IoU) oli 0,82, kun algoritmin ennusteita verrattiin immunohistokemiallisiin CD45-värjäyksiin. Yhteenvetona tuloksista voidaan todeta, että syväoppimiseen perustuvalla algoritmilla voidaan tunnistaa ja kvantitoida sekä korkeasolut että TIL:t papillaarisesta karsinoomasta. Korkeasolujen kynnysarvo 30 % korreloi taudin lyhentyneeseen uusiutumisaikaan ja sitä tulisi käyttää TC-PTC:n diagnostiikassa raja-arvona. Lisäksi kaikki tapaukset, joissa TC-osuus on yli 10 % (PTC, jossa on korkeasoluisia piirteitä), tulisi raportoida patologin toimesta. Väitöskirjan toisena päälöydöksenä totesimme, että immunohistokemiallisesti uudelleenvärjätyt kudosleikkeet soveltuvat erinomaisesti syväoppimiseen perustavan algoritmin kouluttamiseen, kuten TIL-rikkaiden alueiden segmentointiin HE-värjätyistä kudosleikkeistä

HBTQ-Migration En kvalitativ studie om verksamheter i Europa som riktar sig till nyanlända hbtq-personer

The topic of this study is organizations in Europe that work with LGBT-refugees, -asylum seekers or other newcomers. This is a relatively new field in Europe, but some studies have been produced in mainly Canada, focusing at least partly on the same questions. The purpose of this study is to describe the situations this group is facing and also to illustrate in what ways organizations support this group. The methodology of this study is qualitative interviews with participants working at the organizations. The theories used for this study are different support categories and heteronormativity. Findings shows different needs in this group, that the organizations can play an important part in the lives of people in this group and how different kind of support are connected

Nationella minoriteter och det kommunala ansvaret: En fallstudie om Uddevalla kommun och den sverigefinska minoriteten

Minority languages has in a historical perspective been marginalised in Sweden. At the year of 2000 were 5 national minorities and their languages officially recognised as minority languages (Finnish, Meänkieli, Sami, Romani and Yiddish). Nine years later a Swedish law on minority rights was introduced (SFS 2009:724). The law put a large responsibility on municipalities to protect and promote the minority languages. The law also created an opportunity for municipalities to voluntarily become an administrative area for three of the minority languages (Finnish, Meänkieli and Sami). In the administrative areas are the minority rights strengthen. This study examines the case of Uddevalla municipality and the national minority Swedish Finns. This minority is struggling to retain their language and Uddevalla municipality has become an administrative area for the Finnish language. The purpose is to examine views of different types of work in the public administration who aims to strengthen and preserve the Finnish minority language. More specifically it focuses on implementation, accountability and the multicultural ideology which the minority rights framework is built upon. The data used in the study are both interviews with the Swedish minority representatives, public officials, politicians and official documents from Uddevalla municipality. Therefore, the method for the data collection in this study is qualitative interviews. The data were analysed by using a type of qualitative content analysis. The theories used are connected to implementation of political decisions but also to multiculturalism. Along with multiculturalism is also assimilation used as a theoretical concept. Findings shows that multicultural arguments weren’t used when Uddevalla became an administrative area for the Finnish language. Moreover, it identifies different types of important actors in the implementation of minority rights. The implementation is operated in a satisfying way, but there are organisational issues related to it. The responsibility of Uddevalla municipality for the minority is seen as part of a broader work with culture diversity and inclusion. It also shows strong views for the preservation of the Finnish language in the municipality

Antibody Supervised Training of a Deep Learning Based Algorithm for Leukocyte Segmentation in Papillary Thyroid Carcinoma

The quantity of leukocytes in papillary thyroid carcinoma (PTC) potentially have prognostic and treatment predictive value. Here, we propose a novel method for training a convolutional neural network (CNN) algorithm for segmenting leukocytes in PTCs. Tissue samples from two retrospective PTC cohort were obtained and representative tissue slides from twelve patients were stained with hematoxylin and eosin (HE) and digitized. Then, the HE slides were destained and restained immunohistochemically (IHC) with antibodies to the pan-leukocyte anti CD45 antigen and scanned again. The two stain-pairs of all representative tissue slides were registered, and image tiles of regions of interests were exported. The image tiles were processed and the 3,3'-diaminobenzidine (DAB) stained areas representing anti CD45 expression were turned into binary masks. These binary masks were applied as annotations on the HE image tiles and used in the training of a CNN algorithm. Ten whole slide images (WSIs) were used for training using a five-fold cross-validation and the remaining two slides were used as an independent test set for the trained model. For visual evaluation, the algorithm was run on all twelve WSIs, and in total 238,144 tiles sized 500x500 pixels were analyzed. The trained CNN algorithm had an intersection over union of 0.82 for detection of leukocytes in the HE image tiles when comparing the prediction masks to the ground truth anti CD45 mask. We conclude that this method for generating antibody supervised annotations using the destain-restain IHC guided annotations resulted in high accuracy segmentations of leukocytes in HE tissue images.Peer reviewe

The prognostic significance of tall cells in papillary thyroid carcinoma

Background The subtype of the papillary thyroid carcinoma tall cell variant (TCV) has a worse prognosis than does the conventional papillary type (PTC). The new WHO 2017 classification defines a TCV as a tumor consisting of over 30% of cells that are two or three times as tall as they are wide. However, thresholds have differed. Our aim was to study how tall cells affect the prognosis of PTC patients and to determine, for such cells, a cut-off percentage. Methods Our cohort included 65 PTC patients who underwent surgery at Helsinki University Hospital between 1973 and 1996: originally 36 otherwise-matched patient pairs, eventually comprising 34 patients with an adverse outcome and 31 who had recovered. All samples were digitally scanned and scored by two investigators based on tall cell composition. The cohort was analyzed with four tall cell (TC) thresholds: 10%, 30%, 50%, and 70% with a median follow-up of 22 years. Results In survival analysis, only the 70% threshold showed a correlation with reduced overall survival (OS), disease-specific survival (DSS), and relapse-free survival (RFS). A correlation also emerged with death from PTC. In a multivariate analysis, a 70% cut-off and age at diagnosis significantly affected DSS. Conclusion A TC composition of 10%, 30%, or 50% showed no correlation with adverse outcome, and suggests that a 70% threshold should be the choice of pathologists reporting TCV. Our results thus fail to support the new WHO classification.Undergruppen lång cell variant (TCV) till papillär sköldkörtelcancer (PTC) har en sämre prognos än den konventionella varianten. I Världshälsoorganisationens 2017 utkomna upplaga klassas TCV som en tumör som till minst 30% består av celler vars längd är två till tre gånger bredden. Andelen långa celler som behövs har dock varierat kraftigt bland studier. Vårt mål var att undersöka hur andelen långa celler påverkar prognosen för PTC patienter. Därtill ville vi bestämma ett tröskelvärde för andelen långa celler som korrelerar med en sämre prognos. Vår studie inkluderade 65 PTC patienter som blivit opererade på Helsingfors Universitetssjukhus mellan 1973 och 1996. Ursprungligtvis bestod kohorten av 36 matchade patientpar. Efter bortfall p.g.a. avsaknaden av material bestod slutgiltiga kohorten av 34 patienter med dålig prognos och 31 som återhämtat sig. Patienternas histologiska prov överfördes till digitalt format och andelen långa celler räknades av två forskare. Kohorten undersöktes därefter med fyra tröskelvärden för andelen långa celler: 10%, 30%, 50% och 70%. Mediantiden för uppföljning var 22 år. I den resulterande korrelerade endast ett 70% tröskelvärde med en sämre överlevnad. Därtill korrelerade 70% med död till följd av PTC. I en analys av multipla variabler korrelerades ett 70% tröskelvärde med ålder vid diagnos och sjukdomsspecifik överlevnad. Tröskelvärden 10%, 30% och 50% för långa celler visade ingen korrelation med en sämre prognos. Sammanfattningsvis bör ett tröskelvärde av 70% långa celler i PTC användas av patologer vid diagnostisering av TCV

Supplementary_Table – Supplemental material for The prognostic significance of tall cells in papillary thyroid carcinoma: A case-control study

<p>Supplemental material, Supplementary_Table for The prognostic significance of tall cells in papillary thyroid carcinoma: A case-control study by Sebastian Stenman, Päivi Siironen, Harri Mustonen, Johan Lundin, Caj Haglund and Johanna Arola in Tumor Biology</p