18F-FDG PET-Derived Volume-Based Parameters to Predict Disease-Free Survival in Patients with Grade III Breast Cancer of Different Molecular Subtypes Candidates to Neoadjuvant Chemotherapy

We investigated whether baseline [F-18] Fluorodeoxyglucose (F-18-FDG) positron emission tomography (PET)-derived semiquantitative parameters could predict disease-free survival (DFS) in patients with grade III breast cancer (BC) of different molecular subtypes candidate to neoadjuvant chemotherapy (NAC). For each F-18-FDG-PET/CT scan, the following parameters were calculated in the primary tumor (SUVmax, SUVmean, MTV, TLG) and whole-body (WB_SUVmax, WB_MTV, and WB_TLG). Receiver operating characteristic (ROC) analysis was used to determine the capability to predict DFS and find the optimal threshold for each parameter. Ninety-five grade III breast cancer patients with different molecular types were retrieved from the databases of the University Hospital of Padua and the University Hospital of Ferrara (luminal A: 5; luminal B: 34; luminal B-HER2: 22; HER2-enriched: 7; triple-negative: 27). In luminal B patients, WB_MTV (AUC: 0.75; best cut-off: WB_MTV > 195.33; SS: 55.56%, SP: 100%; p = 0.002) and WB_TLG (AUC: 0.73; best cut-off: WB_TLG > 1066.21; SS: 55.56%, SP: 100%; p = 0.05) were the best predictors of DFS. In luminal B-HER2 patients, WB_SUVmax was the only predictor of DFS (AUC: 0.857; best cut-off: WB_SUVmax > 13.12; SS: 100%; SP: 71.43%; p < 0.001). No parameter significantly affected the prediction of DFS in patients with grade III triple-negative BC. Volume-based parameters, extracted from baseline F-18-FDG PET, seem promising in predicting recurrence in patients with grade III luminal B and luminal B- HER2 breast cancer undergoing NAC

Challenges in the management of urothelial cancer : novel treatment, evaluation of biomarkers, and imaging techniques

Urothelial cancer (UC) is the most common malignancy found in the urinary tract. The global annual incidence is approximately 430 000 new cases (Sweden: 3 200 new cases). Approximately one in four new UC patients being diagnosed has muscle-invasive disease. For curative intent, treatment involving surgical removal of the primary tumour remains the gold standard for locally advanced UC. Still, one in two patients relapses despite undergoing curative intended surgery or bladder-preserving radiotherapy. Platinum-containing regimens have been the standard treatment since the 1980s, despite only reaching an overall survival of about 1 year. During the last decade, merely one new chemotherapy has been approved for metastatic UC: vinflunine. The primary aim of this thesis was to improve the management of advanced and metastatic UC by evaluating experimental treatments and exploring predictive and prognostic biomarkers. Paper I describes a patient with metastatic UC and with no available standard treatment options after failing platinum treatment. The patient received the tyrosine kinas inhibitor sorafenib in second-line for almost one year. Immunohistochemistry (IHC) analysis of this patient’s tumour revealed intermediate expression of vascular endothelial growth factor receptor 2 (VEGFR2) and high expression of platelet-derived growth factor receptor β, two key targets of sorafenib. In Paper II, the prognostic value of S100A4, S100A6, and VEGFR2, markers of metastasis, proliferation and angiogenesis, were analysed by IHC in tumour specimen from 83 UC patients following cystectomy of the urinary bladder. Expressions of these proteins were compared with overall and disease-free survival. High expression of VEGFR2 and low tumour stage were independently correlated with longer survival. No association was found for S100A4 or S100A6 in this cohort. The Phase I trial Vinsor (Paper III) was the first clinical study to assess safety of vinflunine plus sorafenib in metastatic UC patients, refractory to platinum. Primary endpoint was to define the recommended Phase II dose (RPTD). In patients treated with a start dose of vinflunine 280 mg/m2 the RPTD of sorafenib was 400 mg. In patients receiving vinflunine 320 mg/m2, the RPTD was not determined because of toxicity. The median overall survival was 7.0 months and the overall response rate was 41%. Predicting early response to treatment is of clinical importance in improving outcome. In Paper IV the predictive value of response evaluation with early 18F-FDG PET scans and plasma exosomes were analysed in a subset of Vinsor trial patients (Paper III). Results demonstrated that early changes on 18F-FDG PET predicted survival and RECIST based on subsequent CT scans. Plasma exosomes could be isolated and quantified, but analysis revealed no association to treatment response. In Paper V, the cytotoxic properties of the peptidase-enhanced alkylating agent melflufen was studied in vitro. In UC cell lines melflufen increased cell death compared to melphalan. Aminopeptidases were found to be of importance for melflufen efficacy in vitro and high expression of aminopeptidase N expression in UC tumour specimens was associated with longer overall survival. In summary, the results of this thesis indicate that subsets of UC patients may have a clinical benefit of sorafenib and that combined treatment with vinflunine is safe and possibly increases treatment efficacy. VEGFR2 appears to have prognostic potential besides being a target for therapy. Early treatment assessment of metastatic UC patients with 18F-FDG PET holds predictive potential. Melflufen shows antitumoral effects in UC cell lines and could be a future novel chemotherapy against this cancer

Utility of hybrid SPECT/CT in Sentinel Lymph Node mapping, and 18F FDG-PET/CT for treatment response evaluation in cancer patients.

The Sentinel Lymph Node Biopsy (SLNB) method is currently well established in the staging of clinically node-negative breast cancer. However, there is some debate concerning the reliability of this method following previous breast surgery. The SLNB method may also be a valuable tool in the staging of oesophageal cancer or cancer of the gastro-oesophageal junction (GOJ), though there are also indications that the method may be less reliable in more advanced cases. Moreover, the impact of a history of neoadjuvant treatment with either chemo-radiotherapy or chemotherapy alone on lymphatic drainage patterns from the oesophagus or GOJ is not well understood. Therefore, there exists a need to further investigate the SLNB method in this patient group. The addition of neoadjuvant therapy in patients with cancer of the oesophagus or GOJ has been shown to improve long-term survival when compared to surgery alone, but there is a need for better diagnostic tools to evaluate the clinical effects of neoadjuvant therapy in this patient group. This thesis had two main aims. The first aim was to evaluate the utility of hybrid SPECT/CT lymphoscintigraphy in patients with lesions of the breast, or lesions of the oesophagus or GOJ. The second aim was to evaluate the predictive value of 18F-FDG PET/CT in regard to histological response following neoadjuvant treatment in patients with cancer of the oesophagus or GOJ. Paper I: In this study including patients with benign breast lesions, and using SPECT/CT lymphoscintigraphy prior to, and six weeks following a diagnostic breast excision, with the non-operated breasts serving as a control group. We observed no statistically significant differences in reproducibility between the operated and non-operated breasts regarding SLN detection. Paper II: In this study including patients with cancer of the oesophagus/GOJ and using hybrid SPECT/CT lymphoscintigraphy. SPECT/CT yielded a high number of detected Sentinel Lymph Nodes. Another aim was to investigate the overall performance of the SLNB method in this patient group, however the accuracy of the Sentinel Lymph Node Biopsy method in the current patient population was poor. Paper III: In this study investigating the effect of neoadjuvant chemo-radiotherapy on tumour lymphatic drainage patterns in patients with cancer of the oesophagus or GOJ using sequential SPECT/CT lymphoscintigraphy before and following chemo-radiotherapy, but before surgery. The reproducibility of SLN detection was very poor. The SLNB method may be unreliable in patients with cancer of the oesophagus/GOJ with a history of previous neoadjuvant chemo-radiotherapy or chemotherapy. Neoadjuvant chemo-radiotherapy in fact appears to have a considerable impact on lymphatic drainage patterns from the oesophagus or GOJ regarding SLN detection. Paper IV: In this study including patients with cancer of the oesophagus/GOJ. randomised to either neoadjuvant chemo-radiotherapy or neoadjuvant chemotherapy and using consecutive 18F-FDG PET/CT examinations. Changes in PET parameters were studied in relation to post- operative histological response in the primary tumour. In particular, changes to the hitherto seldom-used Standardized Uptake Ratio (SUR) PET-parameter was of interest. When pooling the two treatment arms, there was found to be a statistically significant difference in reduction of SUR in patients with histological response compared to patients with little or no histological response. However, it was not possible to predict a complete histological response

Assessment of Healthy Tissue Metabolism to Predict Outcomes in Oncologic [18F]FDG PET/CT

Background: The use of 2-[18F]Fluoro-2-deoxy-d-glucose ([18F]FDG) in positron emission tomography/computed tomography (PET/CT) imaging is not specific to oncologic applications but reflects various pathologic processes with high metabolic activity. Thus, evaluating healthy tissue metabolism (HTM) based on [18F]FDG in cancer patients receiving cytotoxic anti-cancer treatment may provide prognostic information which could potentially assist in identifying patients at high risk of developing treatment-related adverse events (AEs) and those who may have poor outcome. However, unlike cancer imaging HTM assessment with [18F]FDG is lacking standardization in the research setting.Purpose: The main aim of this thesis was to review the applications of [18F]FDG PET/CT in the assessment of anti-cancer treatment-related AEs and to assess methods used in the literature to measure HTM. Further, to evaluate the repeatability and interobserver variation of HTM in lung cancer patients. Finally, HTM based on [18F]FDG uptake was assessed as an imaging biomarker to predicts AEs and outcomes in Hodgkin lymphoma (HL) patients. Methods: A comprehensive literature search was conducted in PubMed, Embase and Web of Science databases for published data on [18F]FDG uptake in different HT for assessment of AEs in cancer patients. Different common and modified methods of assessment were applied to measure [18F]FDG uptake in liver, spleen and other HT. Retrospective test-retest repeatability and interobserver analyses of HTM were also performed on 22 patients with non-small cell lung cancer who underwent [18F]FDG PET/CT of the thorax 2 days apart without intervening treatment (from a prospective study) to measure the maximum, mean and peak standardised uptake values (SUVmax, SUVmean and SUVpeak). Moreover, [18F]FDG uptake in 200 patients with advanced HL from the RATHL trial was retrospectively measured in bone marrow (BM), mediastinal blood pool (MBP), liver and spleen at baseline (PET0) and after 2 cycles of chemotherapy (PET2). Results: Out of the reviewed studies, (n = 80, 94%) reported an association between [18F]FDG uptake in HT and treatment-related AEs. Quantitative assessment using SUVmean was mainly applied in those studies to assess changes in HTM at multiple timepoint. Further evaluation of the liver, spleen and other HT showed that using SUVmean reduces bias across different methods. Further, applying fixed volume of interest (VOI) was comparable to more sophisticated approaches. In comparison to other PET metrics, SUVmean also showed better repeatability as expressed with the within-subject coefficient of variation (wCV) of 20% and high interobserver agreement of ≤10% in HT in the thorax; however, left ventricle uptake was highly variable in a test-retest analysis. In HL, HT uptake changed significantly during treatment. BM uptake at PET0 was associated with baseline haematological parameters, higher risk of neutropenia at cycles 1-2 and failure of early response. Non-responding patients with high BM uptake at PET2 had inferior progression-free survivor (PFS). Conclusion: Most of the studies reviewed from the literature reported an association between HTM and treatment-related AEs among different cancer types and treatment modalities. SUVmean was mainly used in those studies to correlate changes in HTM with treatment-related AEs which was shown to be more stable than SUVmax and SUVpeak. [18F]FDG uptake in uninvolved BM has a prognostic value in HL

Cancer Outcome Prediction with Multiform Medical Data using Deep Learning

This thesis illustrated the work done for my PhD project, which aims to develop personalised cancer outcome prediction models using various types of medical data. A predictive modelling workflow that can analyse data with different forms and generate comprehensive outcome prediction was designed and implemented on a variety of datasets. The model development was accompanied by applying deep learning techniques for multivariate survival analysis, medical image analysis and sequential data processing. The modelling workflow was applied to three different tasks: 1. Deep learning models were developed for estimating the progression probability of patients with colorectal cancer after resection and after different lines of chemotherapy, which got significantly better predictive performance than the Cox regression models. Besides, CT-based models were developed for predicting the tumour local response after chemotherapy of patients with lung metastasis, which got an AUC of 0. 769 on disease progression detection and 0.794 on treatment response classification. 2. Deep learning models were developed for predicting the survival state of patients with non-small cell lung cancer after radiotherapy using CT scans, dose distribution and disease and treatment variables. The eventual model obtained by ensemble voting got an AUC of 0.678, which is significantly higher than the score achieved by the radiomics model (0.633). 3. Deep-learning-aided approaches were used for estimating the progression risk for patients with solitary fibrous tumours using digital pathology slides. The deep learning architecture was able to optimise the WHO risk assessment model using automatically identified levels of mitotic activity. Compared to manual counting given by pathologists, deep-learning-aided mitosis counting can re-grade the patients whose risks were underestimated. The applications proved that the predictive models based on hybrid neural networks were able to analyse multiform medical data for generating data-based cancer outcome prediction. The results can be used for realising personalised treatment planning, evaluating treatment quality, and aiding clinical decision-making

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Investigation of intra-tumour heterogeneity to identify texture features to characterise and quantify neoplastic lesions on imaging

The aim of this work was to further our knowledge of using imaging data to discover image derived biomarkers and other information about the imaged tumour. Using scans obtained from multiple centres to discover and validate the models has advanced earlier research and provided a platform for further larger centre prospective studies. This work consists of two major studies which are describe separately: STUDY 1: NSCLC Purpose The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC). Patients and methods Pre-therapy PET scans from 358 Stage I–III NSCLC patients scheduled for radical radiotherapy/chemoradiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. Using a semiautomatic threshold method to segment the primary tumors, radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients. Results Of 358 patients, 249 died within the follow-up period [median 22 (range 0–85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16–2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information. Conclusion PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy. STUDY 2: Ovarian Cancer Purpose The 5-year survival of epithelial ovarian cancer is approximately 35-40%, prompting the need to develop additional methods such as biomarkers for personalised treatment. Patient and Methods 657 texture features were extracted from the CT scans of 364 untreated EOC patients. A 4-texture feature ‘Radiomic Prognostic Vector (RPV)’ was developed using machine learning methods on the training set. Results The RPV was able to identify the 5% of patients with the worst prognosis, significantly improving established prognostic methods and was further validated in two independent, multi-centre cohorts. In addition, the genetic, transcriptomic and proteomic analysis from two independent datasets demonstrated that stromal and DNA damage response pathways are activated in RPV-stratified tumours. Conclusion RPV could be used to guide personalised therapy of EOC. Overall, the two large datasets of different imaging modalities have increased our knowledge of texture analysis, improving the models currently available and provided us with more areas with which to implement these tools in the clinical setting.Open Acces