460 research outputs found

    Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

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    Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis

    Development and validation of novel and quantitative MRI methods for cancer evaluation

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    Quantitative imaging biomarkers (QIB) offer the opportunity to further the evaluation of cancer at presentation as well as predict response to anti-cancer therapies before and early during treatment with the ultimate goal of truly personalised medical care and the mitigation of futile, often detrimental, therapy. Few QIBs are successfully translated into clinical practice and there is increasing recognition that rigorous methodologies and standardisation of research pipelines and techniques are required to move a theoretically useful biomarker into the clinic. To this end, I have aimed to give an overview of what I believe to be some of key elements within the research field beginning with the concept of imaging biomarkers, introducing concepts in development and validation, before providing a summary of the current and future utility of a range of quantitative MR imaging biomarkers techniques within the oncological imaging field. The original, prospective, research moves from the technical and analytical validation of a novel QIB use (T1 mapping in cancer), first in vivo qualification of this biomarker in cancer patient response assessment and prediction (sarcoma and breast cancer as well as prostate cancer separately), and then moving on to application of more established QIBs in cancer evaluation (R2*/BOLD imaging in head and neck cancer) as well as how existing MR data can be post-processed to improved cancer evaluation (further metrics derived from diffusion weighted imaging in head and neck cancer and textural analysis of existing clinical MR images utility in prostate cancer detection)

    BET inhibitor trotabresib in heavily pretreated patients with solid tumors and diffuse large B-cell lymphomas

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    B-cell lymphoma; Cancer therapy; CNS cancerLimfoma de cèl·lules B; Teràpia del càncer; Càncer del SNCLinfoma de células B; Terapia del cáncer; Cáncer del SNCBromodomain and extraterminal proteins (BET) play key roles in regulation of gene expression, and may play a role in cancer-cell proliferation, survival, and oncogenic progression. CC-90010-ST-001 (NCT03220347) is an open-label phase I study of trotabresib, an oral BET inhibitor, in heavily pretreated patients with advanced solid tumors and relapsed/refractory diffuse large B-cell lymphoma (DLBCL). Primary endpoints were the safety, tolerability, maximum tolerated dose, and RP2D of trotabresib. Secondary endpoints were clinical benefit rate (complete response [CR] + partial response [PR] + stable disease [SD] of ≥4 months’ duration), objective response rate (CR + PR), duration of response or SD, progression-free survival, overall survival, and the pharmacokinetics (PK) of trotabresib. In addition, part C assessed the effects of food on the PK of trotabresib as a secondary endpoint. The dose escalation (part A) showed that trotabresib was well tolerated, had single-agent activity, and determined the recommended phase 2 dose (RP2D) and schedule for the expansion study. Here, we report long-term follow-up results from part A (N = 69) and data from patients treated with the RP2D of 45 mg/day 4 days on/24 days off or an alternate RP2D of 30 mg/day 3 days on/11 days off in the dose-expansion cohorts (parts B [N = 25] and C [N = 41]). Treatment-related adverse events (TRAEs) are reported in almost all patients. The most common severe TRAEs are hematological. Toxicities are generally manageable, allowing some patients to remain on treatment for ≥2 years, with two patients receiving ≥3 years of treatment. Trotabresib monotherapy shows antitumor activity, with an ORR of 13.0% (95% CI, 2.8–33.6) in patients with R/R DLBCL (part B) and an ORR of 0.0% (95% CI, 0.0–8.6) and a CBR of 31.7% (95% CI, 18.1–48.1) in patients with advanced solid tumors (part C). These results support further investigation of trotabresib in combination with other anticancer agents.This study was sponsored by Celgene, a Bristol Myers Squibb Company. The study sponsor was involved in the study design, analysis of data, and writing the manuscript. Medical writing and editorial assistance were provided by Bernard Kerr, PGDipSci, and Agata Shodeke, of Spark, funded by Bristol Myers Squibb

    Treatment Outcome Prediction in Locally Advanced Cervical Cancer: A Machine Learning Approach using Feature Selection on Multi-Source Data

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    Cancer is a significant global health issue, and cervical cancer, one of the most common types among women, has far-reaching impacts worldwide. Researchers are studying cervical cancer from various perspectives, conducting thorough investigations, and utilizing novel technologies to gain a deeper understanding of the disease and its risk factors. Machine learning has increasingly found applications in cancer research due to its ability to analyze complex data relationships, recognize patterns, adapt to new information, and integrate with other technologies. By harnessing predictive machine learning models to anticipate treatment outcomes before commencing any therapies, healthcare providers might be able to make more informed decisions, allocate resources effectively, and provide personalized care. Despite significant efforts in the scientific community, the development of accurate machine learning models for cervical cancer treatment outcome prediction faces several open challenges and unresolved questions. A major challenge in developing accurate prediction models is the limited availability and quality of data. The quantity and quality of data differ across various datasets, which can significantly affect the performance and applicability of machine learning models. Additionally, it is crucial to identify the most informative and relevant features from diverse data sources, including clinical, imaging, and molecular data, to ensure accurate outcome prediction. Moreover, cancer datasets often suffer from class imbalance. Addressing this issue is another essential step to prevent biased predictions and enhance the overall performance of the models. This study aims to improve the prediction of treatment outcomes in patients with locally advanced cervical cancer by utilizing a multi-source dataset and developing different machine-learning models. The dataset includes various data sources, such as medical images, gene scores, and clinical data. A preprocessing pipeline is developed to optimize the data for training machine-learning models. The Repeated Elastic Net Technique (RENT) is also employed as a feature selection method to reduce dataset dimensionality, improve model training time, and identify the most influential features for classifying patients' treatment results. Furthermore, the Synthetic Minority Oversampling Technique (SMOTE) is used to address data imbalance in the dataset, and its impact on model performance is assessed. The study's findings indicate that the available data exhibit promising capabilities in early predicting patients' treatment outcomes, suggesting that the developed models have the potential to serve as valuable auxiliary tools for medical professionals. Although the performance of the models remained relatively unchanged after implementing the RENT method, the models' average training time was reduced by over 8-fold in the worst case. Moreover, when imposing stricter feature selection criteria, clinical features were shown to have a more prominent role in predicting treatment results than other data sources. Ultimately, the study revealed that by balancing the dataset using the SMOTE technique, the average performance of specific models could be enhanced by up to 44 times

    Advancing clinical evaluation and diagnostics with artificial intelligence technologies

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    Machine Learning (ML) is extensively used in diverse healthcare applications to aid physicians in diagnosing and identifying associations, sometimes hidden, between dif- ferent biomedical parameters. This PhD thesis investigates the interplay of medical images and biosignals to study the mechanisms of aging, knee cartilage degeneration, and Motion Sickness (MS). The first study shows the predictive power of soft tissue radiodensitometric parameters from mid-thigh CT scans. We used data from the AGES-Reykjavik study, correlating soft tissue numerical profiles from 3,000 subjects with cardiac pathophysiologies, hy- pertension, and diabetes. The results show the role of fat, muscle, and connective tissue in the evaluation of healthy aging. Moreover, we classify patients experiencing gait symptoms, neurological deficits, and a history of stroke in a Korean population, reveal- ing the significant impact of cognitive dual-gait analysis when coupled with single-gait. The second study establishes new paradigms for knee cartilage assessment, correlating 2D and 3D medical image features obtained from CT and MRI scans. In the frame of the EU-project RESTORE we were able to classify degenerative, traumatic, and healthy cartilages based on their bone and cartilage features, as well as we determine the basis for the development of a patient-specific cartilage profile. Finally, in the MS study, based on a virtual reality simulation synchronized with a moving platform and EEG, heart rate, and EMG, we extracted over 3,000 features and analyzed their importance in predicting MS symptoms, concussion in female ath- letes, and lifestyle influence. The MS features are extracted from the brain, muscle, heart, and from the movement of the center of pressure during the experiment and demonstrate their potential value to advance quantitative evaluation of postural con- trol response. This work demonstrates, through various studies, the importance of ML technologies in improving clinical evaluation and diagnosis contributing to advance our understanding of the mechanisms associated with pathological conditions.Tölvulærdómur (Machine Learning eða ML) er algjörlega viðurkennt og nýtt í ýmsum heilbrigðisþjónustuviðskiptum til að hjálpa læknunum við að greina og finna tengsl milli mismunandi líffærafræðilegra gilda, stundum dulinna. Þessi doktorsritgerð fjallar um samspil læknisfræðilegra mynda og lífsmerkja til að skoða eðli aldrunar, niðurbrot hnéhringjar og hreyfikerfissjúkdóms (Motion Sickness eða MS). Fyrsta rannsóknin sýnir spárkraft midjubeins-CT-skanna í því að fullyrða staðfest- ar meðalþyngdarlíkön, þar sem gögn úr AGES-Reykjavik-rannsókninni eru tengd við hjarta- og æðafræðilega sjúkdóma, blóðþrýstingsveikindi og sykursýki hjá 3.000 þátt- takendum. Niðurstöðurnar sýna hlutverk fitu, vöðva og tengikjarna í mati á heilbrigð- um öldrun. Þar að auki flokkum við sjúklinga sem upplifa gangvandamál, taugaein- kenni og sögu af heilablóðfalli í kóreanskri þjóð, þar sem einstök gangtaksskoðun er tengd saman við tvískoðun. Önnur rannsóknin setur upp ný tölfræðisfræðileg umhverfisviðmið til matar á hnéhringju með samhengi 2D og 3D mynda sem aflað er úr CT og MRI-skömmtum. Í rauninni höfum við getuð flokkað niðurbrots-, slys- og heilbrigðar hnéhringjur á grundvelli bein- og brjóskmerkja með raun að sækja niðurstöður í umfjöllun um sjúklingar eftir réttu einkasniði. Að lokum, í MS-rannsókninni, notum við myndræn tilraun samþættaða með hreyfan- legan grundvöll og EEG, hjartslátt, EMG þar sem yfir 3.000 aðgerðir eru útfránn og greindir til að átta sig á áhrifum MS, höfuðárás hjá konum sem eru íþróttamenn, lífs- stíl og fleira. Einkenni MS eru aflöguð úr heilanum, vöðvum, hjarta og frá hreyfingum þyngdupunktsins á meðan tilraunin stendur og sýna mög

    Apport de l’IRM structurelle multimodale dans la chirurgie d’épilepsie : le cas de l’épilepsie insulaire

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    L’épilepsie insulaire (ÉI) est une forme rare d’épilepsie focale qui, en raison des défis liés à son diagnostic, est difficilement cernable. De plus, la prise en charge des patients avec ÉI s’avère complexifiée par le fait que cette pathologie est fréquemment résistante aux médicaments anti-crises. Pour ces cas médico-réfractaires, la chirurgie insulaire est une option viable. Cela dit, les patients subissant une telle intervention développent fréquemment des déficits neurologiques postopératoires; heureusement, la grande majorité de ceux-ci récupèrent complètement et rapidement. Or, le mécanisme sous-tendant ce singulier rétablissement fonctionnel demeure à ce jour mal compris. Deux modalités modernes d’IRM structurelle, soit l’analyse d’épaisseur corticale et la tractographie, ont permis, dans les dernières années, de décrire les altérations architecturales caractéristiques et potentiellement diagnostiques de divers types d’épilepsie ainsi que de caractériser les remodelages plastiques qui suivent la chirurgie de l’épilepsie extra-insulaire. Cependant, à ce jour, aucune étude ne s’est encore penchée sur le cas de l’ÉI. De ce fait, les études qui constituent cette thèse exploitent l’IRM structurelle afin, d’une part, de dépeindre les altérations d’épaisseur du cortex et de connectivité de matière blanche associées à l’ÉI et, d’autre part, de définir les réarrangements de connectivité subséquents à la chirurgie insulaire pour contrôle épileptique. Les deux premières études de cette thèse ont révélé que l’ÉI était associée à un pattern majoritairement ipsilatéral d’atrophie corticale et d’hyperconnectivité impliquant principalement des sous-régions insulaires et des régions connectées à l’insula. De manière intéressante, la topologie de ces changements correspondait, au moins en partie, à celle du réseau épileptique de l’ÉI. Ensuite, la troisième étude visait à décrire, par le biais d’une méta-analyse, l’histoire naturelle postopératoire des patients subissant une chirurgie pour ÉI. Cette analyse a, entre autres, confirmé que cette chirurgie était efficace (66.7% de disparition des crises) et qu’elle était fréquemment accompagnée de complications neurologiques (42.5%) qui, dans la plupart des cas, étaient transitoires (78.7% des complications) et récupéraient entièrement dans les trois mois postopératoires (91.6% des complications transitoires). Finalement, la quatrième étude a révélé que la chirurgie pour ÉI était suivie d’altérations de connectivité diffuses et bilatérales. Notamment, les connexions présentant une augmentation de connectivité concernaient particulièrement des régions localisées soit près de la cavité chirurgicale ou dans l’hémisphère controlatéral à l’intervention. De plus, la majorité de ces renforcements structurels se sont produits dans les six premiers mois suivant la chirurgie, un délai comparable à celui durant lequel la majeure partie de la récupération fonctionnelle postopératoire a été observée dans notre méta-analyse. En somme, nos résultats suggèrent que les altérations morphologiques en lien avec l’ÉI peuvent correspondre à son réseau épileptique sous-jacent. La topologie de ces changements pourrait constituer un biomarqueur structurel diagnostique qui aiderait à la reconnaissance de l’ÉI et, concomitamment, favoriserait possiblement un traitement chirurgical plus adapté et plus efficace. De plus, les augmentations de connectivité postopératoires pourraient correspondre à des réponses neuroplastiques permettant de prendre en charge les fonctions altérées par la chirurgie. Nos constats ont ainsi contribué à la caractérisation des mécanismes étayant la singulière récupération fonctionnelle accompagnant la chirurgie pour ÉI. À plus grande échelle, nos travaux offrent un aperçu du potentiel de l’IRM structurelle à assister au diagnostic de l’épilepsie focale ainsi qu’à participer à la description des changements plastiques subséquents à une résection neurochirurgicale.Insular epilepsy (IE) is a rare type of focal epilepsy that is difficult to diagnose. In addition to the challenging nature of IE detection, management of patients with this condition is complicated by the tendency of insular seizures to be resistant to anti-seizure medications. For such medically refractory cases, insular surgery constitutes a viable and long-lasting therapeutic option. That said, patients who undergo an insular resection for seizure control frequently develop postoperative neurological deficits; fortunately, most of these impairments recover fully and rapidly. While this favorable postoperative course contributes to improving the outcome of IE surgery, the mechanism underlying the functional recovery remains unknown. Two contemporary structural MRI modalities, namely cortical thickness analysis and tractography, have recently been used to describe characteristic structural alterations of focal epilepsies and to elucidate the postoperative plastic remodeling associated with surgery for extra-insular epilepsy. While these analyses added to our understanding of several localization-related epilepsies, none specifically studied IE. In this thesis, we exploit structural MRI techniques to, first, depict the alterations of cortical thickness and white matter connectivity in IE and, second, define the progressive rearrangements that follow insular surgery for epilepsy. The first two studies of the current thesis showed that IE is associated with a primarily ipsilateral pattern of cortical thinning and hyperconnectivity that mainly involves insular subregions and insula-connected regions. Interestingly, the topology of these changes corresponded, at least in part, to the epileptic network of IE. Furthermore, the third study aimed to describe, via a meta-analysis, the postoperative outcome of patients undergoing surgery for IE. Among other findings, the analysis revealed that insular surgery was effective (66.7% seizure freedom rate) but was associated with a significant risk of neurological complications (42.5%) which, in most cases, were transient (78.7% of all complications) and recovered fully within three months (91.6% of transient complications). Finally, the fourth study showed that surgery for IE was followed by a diffuse pattern of bilateral structural connectivity changes. Notably, connections exhibiting an increase in connectivity were specifically located near the surgical cavity and in the contralateral healthy hemisphere. In addition, the majority of the structural strengthening occurred in the first six months following surgery, a time course that is consistent with the short delay during which most of the postoperative functional recovery was observed in our meta-analysis. Our results suggest that the morphological alterations in IE may reflect its underlying epileptic network. The topology of these changes may constitute a structural biomarker that could help diagnose IE more readily and, concomitantly, potentially enable a more targeted and more effective surgical treatment. Moreover, the postoperative increases in connectivity may be compatible with compensatory neuroplastic responses, a process that arose to recoup the functions of the injured insular cortex. Our findings have therefore contributed to the characterization of the driving process that supports the striking functional recovery seen following surgery for IE. On a larger scale, our work provides insights into the potential of structural MRI to assist in the diagnosis of focal epilepsy and to describe plastic changes following neurosurgical resections

    Evaluering av maskinlæringsmetoder for automatisk tumorsegmentering

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    The definition of target volumes and organs at risk (OARs) is a critical part of radiotherapy planning. In routine practice, this is typically done manually by clinical experts who contour the structures in medical images prior to dosimetric planning. This is a time-consuming and labor-intensive task. Moreover, manual contouring is inherently a subjective task and substantial contour variability can occur, potentially impacting on radiotherapy treatment and image-derived biomarkers. Automatic segmentation (auto-segmentation) of target volumes and OARs has the potential to save time and resources while reducing contouring variability. Recently, auto-segmentation of OARs using machine learning methods has been integrated into the clinical workflow by several institutions and such tools have been made commercially available by major vendors. The use of machine learning methods for auto-segmentation of target volumes including the gross tumor volume (GTV) is less mature at present but is the focus of extensive ongoing research. The primary aim of this thesis was to investigate the use of machine learning methods for auto-segmentation of the GTV in medical images. Manual GTV contours constituted the ground truth in the analyses. Volumetric overlap and distance-based metrics were used to quantify auto-segmentation performance. Four different image datasets were evaluated. The first dataset, analyzed in papers I–II, consisted of positron emission tomography (PET) and contrast-enhanced computed tomography (ceCT) images of 197 patients with head and neck cancer (HNC). The ceCT images of this dataset were also included in paper IV. Two datasets were analyzed separately in paper III, namely (i) PET, ceCT, and low-dose CT (ldCT) images of 86 patients with anal cancer (AC), and (ii) PET, ceCT, ldCT, and T2 and diffusion-weighted (T2W and DW, respectively) MR images of a subset (n = 36) of the aforementioned AC patients. The last dataset consisted of ceCT images of 36 canine patients with HNC and was analyzed in paper IV. In paper I, three approaches to auto-segmentation of the GTV in patients with HNC were evaluated and compared, namely conventional PET thresholding, classical machine learning algorithms, and deep learning using a 2-dimensional (2D) U-Net convolutional neural network (CNN). For the latter two approaches the effect of imaging modality on auto-segmentation performance was also assessed. Deep learning based on multimodality PET/ceCT image input resulted in superior agreement with the manual ground truth contours, as quantified by geometric overlap and distance-based performance evaluation metrics calculated on a per patient basis. Moreover, only deep learning provided adequate performance for segmentation based solely on ceCT images. For segmentation based on PET-only, all three approaches provided adequate segmentation performance, though deep learning ranked first, followed by classical machine learning, and PET thresholding. In paper II, deep learning-based auto-segmentation of the GTV in patients with HNC using a 2D U-Net architecture was evaluated more thoroughly by introducing new structure-based performance evaluation metrics and including qualitative expert evaluation of the resulting auto-segmentation quality. As in paper I, multimodal PET/ceCT image input provided superior segmentation performance, compared to the single modality CNN models. The structure-based metrics showed quantitatively that the PET signal was vital for the sensitivity of the CNN models, as the superior PET/ceCT-based model identified 86 % of all malignant GTV structures whereas the ceCT-based model only identified 53 % of these structures. Furthermore, the majority of the qualitatively evaluated auto-segmentations (~ 90 %) generated by the best PET/ceCT-based CNN were given a quality score corresponding to substantial clinical value. Based on papers I and II, deep learning with multimodality PET/ceCT image input would be the recommended approach for auto-segmentation of the GTV in human patients with HNC. In paper III, deep learning-based auto-segmentation of the GTV in patients with AC was evaluated for the first time, using a 2D U-Net architecture. Furthermore, an extensive comparison of the impact of different single modality and multimodality combinations of PET, ceCT, ldCT, T2W, and/or DW image input on quantitative auto-segmentation performance was conducted. For both the 86-patient and 36-patient datasets, the models based on PET/ceCT provided the highest mean overlap with the manual ground truth contours. For this task, however, comparable auto-segmentation quality was obtained for solely ceCT-based CNN models. The CNN model based solely on T2W images also obtained acceptable auto-segmentation performance and was ranked as the second-best single modality model for the 36-patient dataset. These results indicate that deep learning could prove a versatile future tool for auto-segmentation of the GTV in patients with AC. Paper IV investigated for the first time the applicability of deep learning-based auto-segmentation of the GTV in canine patients with HNC, using a 3-dimensional (3D) U-Net architecture and ceCT image input. A transfer learning approach where CNN models were pre-trained on the human HNC data and subsequently fine-tuned on canine data was compared to training models from scratch on canine data. These two approaches resulted in similar auto-segmentation performances, which on average was comparable to the overlap metrics obtained for ceCT-based auto-segmentation in human HNC patients. Auto-segmentation in canine HNC patients appeared particularly promising for nasal cavity tumors, as the average overlap with manual contours was 25 % higher for this subgroup, compared to the average for all included tumor sites. In conclusion, deep learning with CNNs provided high-quality GTV autosegmentations for all datasets included in this thesis. In all cases, the best-performing deep learning models resulted in an average overlap with manual contours which was comparable to the reported interobserver agreements between human experts performing manual GTV contouring for the given cancer type and imaging modality. Based on these findings, further investigation of deep learning-based auto-segmentation of the GTV in the given diagnoses would be highly warranted.Definisjon av målvolum og risikoorganer er en kritisk del av planleggingen av strålebehandling. I praksis gjøres dette vanligvis manuelt av kliniske eksperter som tegner inn strukturenes konturer i medisinske bilder før dosimetrisk planlegging. Dette er en tids- og arbeidskrevende oppgave. Manuell inntegning er også subjektiv, og betydelig variasjon i inntegnede konturer kan forekomme. Slik variasjon kan potensielt påvirke strålebehandlingen og bildebaserte biomarkører. Automatisk segmentering (auto-segmentering) av målvolum og risikoorganer kan potensielt spare tid og ressurser samtidig som konturvariasjonen reduseres. Autosegmentering av risikoorganer ved hjelp av maskinlæringsmetoder har nylig blitt implementert som del av den kliniske arbeidsflyten ved flere helseinstitusjoner, og slike verktøy er kommersielt tilgjengelige hos store leverandører av medisinsk teknologi. Auto-segmentering av målvolum inkludert tumorvolumet gross tumor volume (GTV) ved hjelp av maskinlæringsmetoder er per i dag mindre teknologisk modent, men dette området er fokus for omfattende pågående forskning. Hovedmålet med denne avhandlingen var å undersøke bruken av maskinlæringsmetoder for auto-segmentering av GTV i medisinske bilder. Manuelle GTVinntegninger utgjorde grunnsannheten (the ground truth) i analysene. Mål på volumetrisk overlapp og avstand mellom sanne og predikerte konturer ble brukt til å kvantifisere kvaliteten til de automatisk genererte GTV-konturene. Fire forskjellige bildedatasett ble evaluert. Det første datasettet, analysert i artikkel I–II, bestod av positronemisjonstomografi (PET) og kontrastforsterkede computertomografi (ceCT) bilder av 197 pasienter med hode/halskreft. ceCT-bildene i dette datasettet ble også inkludert i artikkel IV. To datasett ble analysert separat i artikkel III, nemlig (i) PET, ceCT og lavdose CT (ldCT) bilder av 86 pasienter med analkreft, og (ii) PET, ceCT, ldCT og T2- og diffusjonsvektet (henholdsvis T2W og DW) MR-bilder av en undergruppe (n = 36) av de ovennevnte analkreftpasientene. Det siste datasettet, som bestod av ceCT-bilder av 36 hunder med hode/halskreft, ble analysert i artikkel IV

    Evidence-based oncology: the use of methodologically complex systematic reviews to inform cancer research and clinical practice

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    Background: Systematic reviews are produced to inform health research and clinical practice, e.g., by identifying research gaps and by formulating recommendations in clinical practice guidelines. Standardised methodology exists for the conduct of systematic reviews of interventions. To answer clinically diverse research questions, new methods are constantly being developed for the systematic synthesis of results from different types of studies. Moreover, constant monitoring of newly available evidence, particularly in clinical areas that are rapidly evolving, is important to ensure the currency of systematic reviews. Objective: The primary objective of this cumulative dissertation was to conduct systematic reviews using new and complex systematic review methods, and to contribute to the further development and refinement of these methods. Secondary objective was to conduct clinically relevant systematic reviews to provide meaningful evidence that can inform clinical practice and health care in oncology. Methods: Two clinically relevant systematic reviews using novel and complex methodological approaches were conducted: Systematic review I: A systematic review with network meta analysis and an adapted living approach to evaluate and compare the benefits and harms of first-line therapies for adults with advanced renal cell carcinoma. Systematic review II: A systematic review with meta-analysis of prognostic factor studies to explore the interim positron emission tomography (PET) scan result as a prognostic factor in adults with newly diagnosed Hodgkin lymphoma. Results: Methodological results Systematic review I: The evidence for the currently recommended treatments and important comparisons in this review stem from direct evidence from one trial per comparison only. This is due to the great lack of head-to-head comparisons of the many treatment options available. Statistical validation of the homogeneity and consistency assumptions was not possible for every network meta-analysis, so the validity of estimates is largely based on the transitivity assumption. When a strong evidence base is missing, the results of a network meta-analysis, including the ranking of treatments, should be interpreted with caution. The adapted living approach, where monthly update searches were conducted during the conduct of the review, was an appropriate method to maintain the currency of the evidence in such a rapidly evolving treatment landscape. Systematic review II: The greatest methodological challenges identified in synthesising evidence from prognostic factor studies were that, firstly, searching for prognosis studies is challenging due to insufficient indexing and missing search filters that are specific and sensitive enough to identify prognostic factor studies. Secondly, extracting and analysing outcome results was particularly difficult due to incomplete reporting of important data in the, usually retrospective, studies. Thirdly, available methods for the quality assessments had to be adapted to fit to the review question. Lastly, methods for the certainty assessment of the evidence from prognosis studies had to be developed during the conduct of the review as there was no official guidance at that time. The challenges encountered during the conduct of both reviews were discussed and resolved through the involvement of methodological and clinical experts as coauthors. Clinical results: Systematic Review I: Combinations of novel therapies (e.g., a checkpoint inhibitor with a tyrosine kinase inhibitor) appear to be superior to monotherapy with sunitinib (a tyrosine kinase inhibitor) as first-line therapy in terms of survival for adults with advanced renal cell carcinoma. However, these novel treatments may cause more (serious) side effects. Moreover, the question on the potential impact of these novel treatments on the quality of life of affected individuals remains unanswered. Systematic Review II: Evidence was found on the prognostic ability of the interim PET-scan result to predict survival in adults with Hodgkin lymphoma. It successfully distinguishes between PET-negative people, who have a better outcome prognosis, and PET-positive people, who have a worse outcome prognosis. Conclusion:Future methodological research needs to further address these different challenges, for example the challenges one encounters when trying to search for and identify prognostic factor studies, or the limitations one encounters when underlying assumptions of a network meta analysis cannot be verified. When evidence from such methodologically complex systematic reviews shall be used to inform clinical practice guidelines and, thereby, health care decision making, all involved stakeholders need to be aware of the methodological complexity and limitations behind the evidence produced
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