Early assessment of tumour response has lately acquired big interest in the medical field, given the possibility to modify treatments during their delivery. Radiomics aims to quantitatively describe images in radiology by automatically extracting a large number of image features. In this context, PET/CT (Positron Emission Tomography/Computed Tomography) images are of great interest since they encode functional and anatomical information, respectively. In order to assess the patients' responses from many image features appropriate methods should be applied. Machine learning offers different procedures that can deal with this, possibly high dimensional, problem. The main objective of this work was to develop a method to classify lung cancer patients as responding or not to chemoradiation treatment, relying on repeated PET/CT images. Patients were divided in two groups, based on the type of chemoradiation treatment they underwent (sequential or concurrent radiation therapy with respect to chemotherapy), but image features were extracted using the same procedure. Support vector machines performed classification using features from the Radiomics field, mostly describing tumour texture, or from handcrafted features, which described image intensity changes as a function of tumour depth. Classification performance was described by the area under the curve (AUC) of ROC (Receiving Operator Characteristic) curves after leave-one-out cross-validation. For sequential patients, 0.98 was the best AUC obtained, while for concurrent patients 0.93 was the best one. Handcrafted features were comparable to those from Radiomics and from previous studies, as for classification results. Also, features from PET alone and CT alone were found to be suitable for the task, entailing a performance better than random.

Personalized medicine is the idea that every patient can be treated in a unique manner, tailored specifically to his or her individual needs. Traditionally the field of personalized medicine has focused on using genetic information to determine medical treatment. However, humans are not only the sum of their genetic parts. All people exist within the context of their environment, their experiences, and their relationships. While the connection between this greater context and medical treatment may not be immediately obvious, it exists. If we are to truly tailor medical care, it must occur in a holistic manner, combining both genetics and social context. A thorough understanding of the way that they interact, as well as the individual limitations of both, is the best way to offer individualized care to all patients. / Urban Bioethics

Background and Aims: In the treatment of hepatocellular carcinoma (HCC), response prediction to transarterial chemoembolization (TACE) based on serum biomarkers is not established. We have studied the association of circulating Dickkopf-related protein 1 (DKK-1) with baseline characteristics and response to TACE in European HCC patients. Methods: Patients with HCC treated with TACE from 2010 to 2018 at a tertiary referral hospital were retrospectively enrolled. Levels of DKK-1 were measured in serum samples collected before TACE. Response was assessed according to mRECIST criteria at week 12 after TACE. Results: Ninety-seven patients were enrolled, including seventy-nine responders and eighteen refractory. Before TACE, median DKK-1 serum levels were 922 [range, 199–4514] pg/mL. DKK-1 levels were lower in patients with liver cirrhosis (p = 0.002) and showed a strong correlation with total radiologic tumor size (r = 0.593; p < 0.001) and with Barcelona Clinic Liver Cancer stages (p = 0.032). Median DKK-1 levels were significantly higher in refractory patients as compared to responders (1471 pg/mL [range, 546–2492 pg/mL] versus 837 pg/mL [range, 199–4515 pg/mL]; p < 0.001), and DKK-1 could better identify responders than AFP (AUC = 0.798 vs. AUC = 0.679; p < 0.001). A DKK-1 cutoff of ≤1150 pg/mL was defined to identify responders to TACE with a sensitivity of 78% and specificity of 77%. DKK-1 levels were suitable to determine response to TACE in patients with low AFP serum levels (AFP levels < 20 ng/mL; AUC = 0.843; 95% CI [0.721–0.965]; p = 0.003). Conclusion: DKK-1 levels in serum are strongly associated tumor size and with response to TACE in European HCC patients, including those patients with low AFP levels.

<p>The use of positron emission tomography (PET) in radiation therapy has continued to grow, especially since the development of combined computed tomography (CT) and PET imaging system in the early 1990s. Today, the biggest use of PET-CT is in oncology, where a glucose analog radiotracer is rapidly incorporated into the metabolic pathways of a variety of cancers. Images representing the in-vivo distribution of this radiotracer are used for the staging, delineation and assessment of treatment response of patients undergoing chemotherapy or radiation therapy. While PET offers the ability to provide functional information, the imaging quality of PET is adversely affected by its lower spatial resolution. It also has unfavorable image noise characteristics due to radiation dose concerns and patient compliance. These factors result in PET images having less detail and lower signal-to-noise (SNR) properties compared to images produced by CT. This complicates the use of PET within many areas of radiation oncology, but particularly the delineation of targets for radiation therapy and the assessment of patient response to therapy. The development of segmentation methods that can provide accurate object identification in PET images under a variety of imaging conditions has been a goal of the imaging community for years. The goal of this thesis are to: (1) investigate the effect of filtering on segmentation methods; (2) investigate whether combining individual segmentation methods can improve segmentation accuracy; (3) investigate whether the consensus volumes can be useful in aiding physicians of different experience in defining gross tumor volumes (GTV) for head-and-neck cancer patients; and (4) to investigate whether consensus volumes can be useful in assessing early treatment response in head-and-neck cancer patients.</p><p>For this dissertation work, standard spherical objects of volumes ranging from 1.15 cc to 37 cc and two irregularly shaped objects of volume 16 cc and 32 cc formed by deforming high density plastic bottles were placed in a standardized image quality phantom and imaged at two contrasts (4:1 or 8:1 for spheres, and 4.5:1 and 9:1 for irregular) and three scan durations (1, 2 and 5 minutes). For the work carried out into the comparison of images filters, Gaussian and bilateral filters matched to produce similar image signal to noise (SNR) in background regions were applied to raw unfiltered images. Objects were segmented using thresholding at 40% of the maximum intensity within a region-of-interest (ROI), an adaptive thresholding method which accounts for the signal of the object as well as background, k-means clustering, and a seeded region-growing method adapted from the literature. Quality of the segmentations was assessed using the Dice Similarity Coefficient (DSC) and symmetric mean absolute surface distance (SMASD). Further, models describing how DSC varies with object size, contrast, scan duration, filter choice and segmentation method were fitted using generalized estimating equations (GEEs) and standard regression for comparison. GEEs accounted for the bounded, correlated and heteroscedastic nature of the DSC metric. Our analysis revealed that object size had the largest effect on DSC for spheres, followed by contrast and scan duration. In addition, compared to filtering images with a 5 mm full-width at half maximum (FWHM) Gaussian filter, a 7 mm bilateral filter with moderate pre-smoothing (3 mm Gaussian (G3B7)) produced significant improvements in 3 out of the 4 segmentation methods for spheres. For the irregular objects, time had the biggest effect on DSC values, followed by contrast. </p><p>For the study of applying consensus methods to PET segmentation, an additional gradient based method was included into the collection individual segmentation methods used for the filtering study. Objects in images acquired for 5 minute scan durations were filtered with a 5 mm FWHM Gaussian before being segmented by all individual methods. Two approaches of creating a volume reflecting the agreement between the individual methods were investigated. First, a simple majority voting scheme (MJV), where individual voxels segmented by three or more of the individual methods are included in the consensus volume, and second, the Simultaneous Truth and Performance Level Estimation (STAPLE) method which is a maximum likelihood methodology previously presented in the literature but never applied to PET segmentation. Improvements in accuracy to match or exceed the best performing individual method were observed, and importantly, both consensus methods provided robustness against poorly performing individual methods. In fact, the distributions of DSC and SMASD values for the MJV and STAPLE closely match the distribution that would result if the best individual method result were selected for all objects (the best individual method varies by objects). Given that the best individual method is dependent on object type, size, contrast, and image noise and the best individual method is not able to be known before segmentation, consensus methods offer a marked improvement over the current standard of using just one of the individual segmentation methods used in this dissertation. </p><p>To explore the potential application of consensus volumes to radiation therapy, the MJV consensus method was used to produce GTVs in a population of head and neck cancer patients. This GTV and one created using simple 40% thresholding were then available to be used as a guidance volume for an attending head and neck radiation oncologist and a resident who had completed their head and neck rotation. The task for each physician was to manually delineate GTVs using the CT and PET images. Each patient was contoured three times by each physician- without guidance and with guidance using either the MJV consensus volume or 40% thresholding. Differences in GTV volumes between physicians were not significant, nor were differences between the GTV volumes regardless of the guidance volume available to the physicians. However, on average, 15-20% of the provided guidance volume lay outside the final physician-defined contour.</p><p>In the final study, the MJV and STAPLE consensus volumes were used to extract maximum, peak and mean SUV measurements in two baseline PET scans and one PET scan taken during patients' prescribed radiation therapy treatments. Mean SUV values derived from consensus volumes showed smaller variability compared to maximum SUV values. Baseline and intratreatment variability was assessed using a Bland-Altman analysis which showed that baseline variability in SUV was lower than intratreatment changes in SUV.</p><p>The techniques developed and reported in this thesis demonstrate how filter choice affects segmentation accuracy, how the use of GEEs more appropriately account for the properties of a common segmentation quality metric, and how consensus volumes not only provide an accuracy on par with the single best performing individual method in a given activity distribution, but also exhibit a robustness against variable performance of individual segmentation methods that make up the consensus volume. These properties make the use of consensus volumes appealing for a variety of tasks in radiation oncology.</p> / Dissertation

Cette thèse présente des travaux de modélisation mathématiquede la réponse au traitement en oncologie appliquée à trois pathologies différentes.La première partie traite du cas des GIST et de la réponse à la thérapie ciblée.Deux modèles sont décrits afin d’étudier différents critères de suivie del’évolution tumorale. La deuxième partie porte sur les tumeurs colorectales etla réponse à la radiothérapie. Un premier modèle d’équations aux dérivées partielles,couplé à un modèle décrivant l’examen du PET-scan, est mis en place,puis il est simplifié afin de rendre la phase de calibration plus rapide. Cetteméthode est testée sur deux cas cliniques.La dernière partie traite des tumeurs ORL sous radiothérapie. Une méthodede personnalisation du modèle, similaire à celle de la partie précédente, est miseen place et est testée sur six cas cliniques. / We first present two mathematical models to simulate the evolution and theresponse to treatments of GIST.Then, we study colorectal tumors and radiotherapy response. We present apartial differential equations model to simulate the tumor evolution, the responseto radiotherapy and the PET-scan. We introduce a simplification of thefirst model to develope a calibration technic based on medical images of thetumor. Two applications on clinical cases are presented.To finish, a similar method is adapted to ORL tumors and response to radiotherapyand tested on six clinical cases.

The Cytochrome P450 family of enzymes is responsible for the majority of Phase I metabolism, and has been identified as an important source of pharmacokinetic variation in therapeutic responses. CYP2C19 and CYP2D6, metabolising >35% of commonly prescribed medications, are two of the most important pharmacogenetic markers that have been studied with the aim of improving treatment response and reducing adverse drug reactions. The Food and Drug Administration (FDA) approved AmpliChip CYP450 Test (AmpliChip) was compared to a previously developed PCR-RFLP platform and a newly developed XLPCR+ Sequencing platform for the ability to identifying genotype and predicting phenotype for CYP2C19 and CYP2D6 respectively. The AmpliChip was found not to be genotypically comprehensive enough for evaluating CYP2C19 genotype, not robust enough for determining CYP2D6 genotype and inaccurate in predicting phenotype for both. The XLPCR+ Sequencing method identified three novel alleles and one sub-variant. Advances in online column-switching solid phase extraction generated a rapid and robust LCMS/ MS method for simultaneously quantifying the probe drugs omeprazole (CYP2C19 substrate), dextromethorphan (CYP2D6 substrate) and their metabolites. Antimodes were identified for phenotypic cut-offs which offered measured phenotype for comparison to predicted phenotype. Omeprazole metabolism by CYP2C19 correlated well with predicted phenotype in a demographically representative South African cohort. There are concerns regarding the use of omeprazole as a probe drug as participants predicted to be ultrarapid metabolisers for CYP2C19 had similar rates to extensive metabolisers. Regardless of this concern, decreased metabolism was assigned to the CYP2C19*15 for the first time. CYP2D6 predicted phenotype correlated very well with measured phenotype, validating the suitability of dextromethorphan use for measuring CYP2D6 metabolism. Substrate modified activity score using 0.5 to predict intermediate metabolisers fine-tuned the XLPCR+ Sequencing platform for phenotype prediction. This finding, along with observations in CYP2C19 metabolism of omeprazole, highlights the importance of substrate specific phenotype prediction strategies. Controversially, attempts to associate CYP2D6 phenotype prediction with risperidone-related adverse drug reactions has yielded conflicting results. The XL-PCR+Sequencing platform was able to discount this association by predicting a variety of metabolisers in a pilot cohort selected to be experiencing risperidone-related adverse drug reactions. The comprehensive capability of the XL-PCR+Sequencing allowed for the identification of an additional novel allele in this cohort. The data presented in thisthesis has provided insight into the relationship between predicted and measured phenotype for CYP2C19 and CYP2D6 in the South African population. The XL-PCR+Sequencing platform can be used for future research or can be applied to improve treatment outcome. The LC-MS/MS method developed could be used for future evaluations of predicted and measured phenotype with the ability to be adjusted for therapeutic drug monitoring. This thesis advances pharmacogenetics of CYP2C19 and CYP2D6 for use in the South African population. / Thesis (PhD)--University of Pretoria, 2013. / gm2014 / Pharmacology / unrestricted

Soňa Fraňková: Role of host-dependent factors in prediction of antiviral treatment response in chronic hepatitis C Abstract Hepatitis C virus infection represents a leading cause of liver disease in western countries. The primary goal of HCV therapy is elimination of the virus, i.e. sustained virological response (SVR) achievement. Genetic factors have long been suspected of playing a crucial role in determining response to IFN-α-based therapies, but pretreatment predictors of response were only poorly defined and did not allow personalization of therapy. The aim of the thesis is to describe the role of host-dependent factors in prediction of antiviral treatment response in chronic hepatitis C in specific groups of patients. First, we focused on the role of the IFNG -764G/C promoter variant in SVR achievement. We did not prove that this variant predicted SVR in Czech HCV-infected individuals. Next, we focused on the role of IL28B and IFNL4 in HCV-infected patients: we confirmed that the IL28B rs12979860 CC genotype slows down the progression of liver fibrosis in chronic HCV infection and that IFNL4 ss469415590 TT|ΔG genotyping does not bring a better prediction of treatment success than IL28B rs12979860 in the Czech population. Third, we assessed prediction of treatment response in HCV positive liver...

This study examines psychotherapy response in connection to treatment duration and maintenance of treatment gains. The dose-effect perspective (Howard et al. 1986) first proposed applying medical terminology to investigate a level of exposure to a dose of psychotherapy (in number of sessions) where individuals can expect to receive sufficient benefit (i.e., 48 -- 58% of clients can be expected to sufficiently benefit from therapy by 8 sessions). The proponents of the Good Enough Level (Barkham et at. 2006) argued that mere exposure to therapy is not an effective measure for client benefit, but rather that client responses to therapy vary. They contend that instead of recommendations for attending a certain number of sessions (dose-effect) that individuals who attend psychotherapy will discontinue attending therapy when they have obtained sufficient benefit (good enough level). Archival data of university students who previously attended individual therapy were obtained and subjects were contacted via email to take a survey and follow up measure of general well being. Those individuals who completed the Outcome Questionnaire-45 were selected for the study and their treatment response was analyzed in connection to treatment duration measured in number of sessions attended. 288 met criteria for the current study, consisting of 197 women and 91 men ranging in age from 17 to 52 (M= 21). Conclusions obtained from this study indicate that treatment duration is not a factor in subjects having positive outcomes to psychotherapy. Additionally, there was not a significant difference among subjects who were able to maintain treatment gains and the number of session attended in treatment. These results offer support for the Good Enough Level model of treatment response suggesting that individuals respond to therapy differently and discontinue when they have received sufficient benefit. Implications for these findings are discussed along with limitations of the current study.

Pancreatic cancer is the seventh leading cause of cancer related mortalities worldwide and incidences are increasing. The prognosis remains poor as the 5-year survival rate is below 10%. This can be partly explained by the silent progression of disease as most patients present with advanced disease at time of diagnosis. In turn, surgical resection, the only potential curative measure, is not possible in nearly 80% of cases due to the occurrence of distant metastasis and/or infiltration of major vessels in close proximity to the pancreas. In patients with localized but advanced disease, resectability can be achieved in some cases by initiation of a neoCTx. However, as neoCTx is commonly conducted by administering multi-drug treatments, severe side effects occur frequently, which require an adaption of drug doses administered. In this study, we revealed the negative impact of these drug dose changes during neoCTx on the patients´ treatment outcome. R0 resections were significantly less frequently observed, and the N-status significantly impacted by the tumor regression grade, which in turn trended towards minor response in the cohort of patients that did not sustain full dose course prior surgery. In turn, treatment of LA PDAC could be improved by increasing the proportion of patients that undergo neoCTx without any changes of the treatment schedule. Patient-derived PDAC organoid could serve as an avatar of patients´ tumor disease on which optimal treatment protocols could be tested. In this study, a large living PDAC PDO biobank successfully has been established from surgical resection specimens as well as EUS guided FNA samples. Subsequently, a new protocol for molecular subtyping of PDAC on organoids was established by assessing the expression level of KRT81 and CFTR, as a replacement for HNF1a, using IF staining. Strikingly, we observed identical PDAC subtypes in PDOs and their respective tissue of origin in nearly all cases. This observation allowed the assumption that PDOs could indeed be used as patient-individual avatars to identify treatment sensitivities and resistances, as they share fundamental molecular properties with the tissue they have been initiated from. Extensive pharmacotyping was performed for many PDO lines by testing the response behavior to the multi-drug regimens FOLFIRINOX and Gem/Pac, as well as their respective single drug compounds. As a result, we observed diverse response patterns for each PDAC PDO line. A poor response to FOLFIRINOX did not necessarily imply a resistance to Gem/Pac. PDO pharmacotyping could guide treatment decision making in the foreseeable future. Moreover, when the non-efficient drug was removed, no changes of overall efficacy of treatment in PDOs was observed, implying that additional therapy improvements could be possible using this ex vivo model. This observation was true for both commonly used chemotherapy protocols, FOLFIRINOX and Gem/Pac and could result in less drug mediated side effects under (neo)adjuvant CTx without impacting treatment efficacy. Yet, the main goal of this study was to assess if PDAC PDOs can be used to predict the neoCTx outcome of PDAC patients. All methods required to address this issue in a prospective clinical trial have been established as a protocol for PDAC PDOs initiation from minimal starting material has been established and subsequently improved resulting in take rates of up to 80%. To support this study, we successfully secured patient enrollment from a second clinical center, which will increase the number of recruited patients in the future. Unfortunately, at the time of writing this thesis, patient numbers were not sufficient to answer the question of the predictive value of PDAC PDOs in regard to the current standard of care.

No description available.