Survival Outcomes of Pancreatic Intraepithelial Neoplasm (PanIN) versus Intraductal Papillary Mucinous Neoplasm (IPMN) Associated Pancreatic Adenocarcinoma

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Pancreatic intraepithelial neoplasms (PanINs) and intraductal papillary mucinous neoplasms (IPMNs) are common pancreatic adenocarcinoma precursor lesions. However, data regarding their respective associations with survival rate and prognosis are lacking. We retrospectively evaluated 72 pancreatic adenocarcinoma tumor resection patients at the University of Kansas Hospital between August 2009 and March 2019. Patients were divided into one of two groups, PanIN or IPMN, based on the results of the surgical pathology report. We compared baseline characteristics, overall survival (OS), and progression free survival (PFS) between the two groups, as well as OS and PFS based on local or distant tumor recurrence for both groups combined. 52 patients had PanINs and 20 patients had IPMNs. Patients who had an IPMN precursor lesion had better median PFS and OS when compared to patients with PanIN precursor lesions. However, the location of tumor recurrence (local or distant) did not show a statistically significant difference in OS

Στατιστικές μέθοδοι αξιολόγησης διαγνωστικών ελέγχων παρουσία λογοκρισίας

Biomarkers play a significant role in decision making and are considered as the key to early diagnosis which may in turn lead to complete cure of a disease or the limitation of its progress. It is desired that the development of new biomarkers will contribute to obtaining more predictive information regarding the disease status of a patient as well as to providing a better understanding concerning the biological mechanism of various diseases. The evaluation of diagnostic markers is a necessary procedure before a medical test is used. In this thesis we will introduce new methodologies that contribute to the evaluation of a marker regarding mainly its diagnostic accuracy in the presence of censoring.Censoring is a phenomenon primarily met in survival analysis in which patients aremonitored over time until they experience an event. This event is usually death whenfatal diseases are involved. In many cases, due to practical, psychological or other rea-sons many patients decide to leave/quit the study (for example due to their relocation toanother country or due to the end of a study when some patients are event-free). Thetime at which these patients will experience the event is unknown to the clinical researchers and the only information available is that they were event-free until a given time. It might be the case that a marker’s measurements change with time and are used to predict the future disease status of a subject. For such biomarkers it is expected that measurements taken closer to the event will be more indicative of the disease or its progression so it is natural to assume that biomarker measurements are related to the time to event variable. Hence, the phenomenon of censoring naturally arises in the concept of evaluating such a biomarker. Censoring can also occur on the biomarker value itself. There are cases where due topractical reasons or technological limitations marker measurements cannot be providedbelow or beyond some known limit, usually called limit of detection (LOD) which mayvary from batch to batch. In such cases the biomarker is itself subject to left or rightcensoring respectively. In this thesis we examine settings where censoring may occur onthe time to event as well as settings where the biomarker measurements themselves aresubject to censoring.Οι διαγνωστικοί έλεγχοι (ή διαγνωστικά τέστ ή βιοδείκτες) παίζουν πρωταρχικό ρόλο στη λήψη κλινικών αποφάσεων μιας και είναι το κλειδί για την έγκαιρη διάγνωση η οποία με τη σειρά της μπορεί να οδηγήσει σε πλήρη θεραπεία μιας ασθένειας ή στον περιορισμό της εξέλιξής της. Είναι επιθυμητό η ανάπτυξη νέων διαγνωστικών ελέγχων να συνεισφέρει τόσο στην παροχή πληροφορίας που αφορά στην προβλεπτική ικανότητά μας για την μελλοντική κατάσταση ενός ασθενούς, όσο και στην καλύτερη κατανόηση του μηχανισμού των υπό μελέτη ασθενειών. Η αξιολόγηση διαγνωστικών ελέγχων είναι μια απαραίτητη διαδικασία πρiν την κλινική τους χρήση. Σε αυτήν την διδακτορική διατριβή αναπτύσουμε νέες μεθοδολογίες που συνεισφέρουν στην αξιολόγηση διαγνωστικών ελέγχων, όσον αφορά στην διαγνωστική τους ακρίβεια, παρουσία λογοκρισίας.Το φαινόμενο της λογοκρισίας το συναντάμε κυρίως στην Ανάλυση Επιβίωσης, όπου ασθενείς παρακολουθοούνται σε βάθος χρόνου μέχρι να υποστούν το υπό μελέτη "γεγονός". Αυτό το γεγονός είναι συνήθως ο θάνατος όταν έχουμε να κάνουμε με καταληκτικές ασθένειες, αλλά μπορεί να οριστεί διαφορετικά ανάλογα με την ασθένεια που μελετάται (πχ μετάσταση, υποτροπή κλπ). Σε πολλές περιπτώσεις, για πρακτικούς, ψυχολογικούς ή άλλους λόγους, πολλοί ασθενείς εγκαταλείπουν τις κλινικές έρευνες (για παράδειγμα για λόγους μετανάστευσης, η εξαιτίας του ότι δεν έχουν υποστεί το γεγονός μέχρι το τέλος της έρευνας). Η χρονική στιγμή κατά την οποία τα εν λόγω άτομα θα υποστούν το γεγονός είναι συνεπώς άγνωστη, και η μόνη πληροφορία που έχουμε είναι ότι δεν είχαν υποστεί το γεγονός μέχρι την στιγμή της αποχώρησής τους από την έρευνα. Υπάρχουν περιπτώσεις που οι διαγνωστικοί έλεγχοι είναι χρονοεξαρτώμενοι και χρησιμοποιούνται για την πρόβλεψη της μελλοντικής κατάστασης ενός ασθενούς. Για τέτοιους διαγνωστικούς ελέγχους είναι αναμενόμενο ότι μετρήσεις που λήφθηκαν σε μικρή χρονική απόσταση από τη στιγμή του γεγονότος, θα είναι και πιο δεικτικές της υπό μελέτη ασθένειας. Συνεπώς το φαινόμενο της λογοκρισίας υπεισέρχεται άμεσα στη διαδικασία της αξιολόγησης τέτοιων διαγνωστικών ελέγχων. Το φαινόμενο της λογοκρισίας μπορεί επίσης να αφορά σε αυτές καθεαυτές τις μετρήσεις του διαγνωστικού ελέγχου. Υπάρχουν περιπτώσεις που λόγω τεχνικών δυσκολιών δε μπορούν να ληφθούν μετρήσεις πάνω ή κάτω από κάποιο όριο ανίχνευσης. Σε τέτοιες περιπτώσεις ο διαγνωστικός έλεγχος είναι λογοκριμένος δεξιά ή αριστερά αντίστοιχα. Σε αυτήν την διδακτορική διατριβή εξετάζουμε περιπτώσεις όπου το φαινόμενο της λογοκρισίας υπεισέρχεται τόσο στον χρόνο μέχρι το γεγονός (όταν μιλάμε για χρονοεξαρτώμενους διαγνωστικούς ελέγχους) όσο και σε αυτές καθεαυτές τις μετρήσεις του διαγνωστικού ελέγχου

Dataset for: Comparison of Two Correlated ROC Surfaces at a Given Pair of True Classification Rates

The receiver operating characteristics (ROC) curve is typically employed when one wants to evaluate the discriminatory capability of a continuous or ordinal biomarker in the case where two groups are to be distinguished, commonly the ’healthy’ and the ’diseased’. There are cases for which the disease status has three categories. Such cases employ the (ROC) surface, which is a natural generalization of the ROC curve for three classes. In this paper, we explore new methodologies for comparing two continuous biomarkers that refer to a trichotomous disease status, when both markers are applied to the same patients. Comparisons based on the volume under the surface have been proposed, but that measure is often not clinically relevant. Here, we focus on comparing two correlated ROC surfaces at given pairs of true classification rates, which are more relevant to patients and physicians. We propose delta-based parametric techniques, power transformations to normality, and bootstrap-based smooth nonparametric techniques to investigate the performance of an appropriate test. We evaluate our approaches through an extensive simulation study and apply them to a real data set from prostate cancer screening

Construction of confidence intervals for the maximum of the Youden index and the corresponding cutoff point of a continuous biomarker.

Evaluation of the overall accuracy of biomarkers might be based on average measures of the sensitivity for all possible specificities -and vice versa- or equivalently the area under the receiver operating characteristic (ROC) curve that is typically used in such settings. In practice clinicians are in need of a cutoff point to determine whether intervention is required after establishing the utility of a continuous biomarker. The Youden index can serve both purposes as an overall index of a biomarker's accuracy, that also corresponds to an optimal, in terms of maximizing the Youden index, cutoff point that in turn can be utilized for decision making. In this paper, we provide new methods for constructing confidence intervals for both the Youden index and its corresponding cutoff point. We explore approaches based on the delta approximation under the normality assumption, as well as power transformations to normality and nonparametric kernel- and spline-based approaches. We compare our methods to existing techniques through simulations in terms of coverage and width. We then apply the proposed methods to serum-based markers of a prospective observational study involving diagnosis of late-onset sepsis in neonates

Statistical inference for the difference between two maximized Youden indices obtained from correlated biomarkers.

Currently, there is global interest in deriving new promising cancer biomarkers that could complement or substitute the conventional ones. Clinical decisions can often be based on the cutoff that corresponds to the maximized Youden index when maximum accuracy drives decisions. When more than one classification criteria are measured within the same individuals, correlated measurements arise. In this work, we propose hypothesis tests and confidence intervals for the comparison of two correlated receiver operating characteristic (ROC) curves in terms of their corresponding maximized Youden indices. We explore delta-based techniques under parametric assumptions, or power transformations. Nonparametric kernel-based methods are also examined. We evaluate our approaches through simulations and illustrate them using data from a metabolomic study referring to the detection of pancreatic cancer

Epidermal Growth Factor Receptor Inhibitor Treatment Timing does not Impact Survival in Stage 4 Colon Cancer Treatment: A Retrospective Study: EGFR Inhibitor Treatment Line Outcomes

Introduction. Colon cancer impacts the lives of Kansans and those across the United State.(1, 2) Epidermal growth factor receptor (EGFR) inhibitors, such as panitumumab and cetuximab, have gained popularity as first-line treatment for stage 4 colon cancer despite their toxicities.(3-5) EGFR inhibitors are an efficacious first-line treatment for stage 4 colon cancer, but no study has investigated outcomes comparing EGFR inhibitors as first-line treatment to it used as second- or third-line treatment. This study investigates EGFR inhibitor therapy estimated survival when used as first-, second-, and third-line. Methods. A retrospective review was done for patients with stage 4 colon cancer who underwent EGFR inhibitor treatment at a large academic center from November 2007 to August 2021. The patients were stratified into five groups by the line in which they received the EGFR inhibitor treatment. A log-rank test was used to analyze the groups, and the median survival for each group was determined. Results. A total of 68 patients were reviewed; 18 received first-line, 23 received second-line, 18 received third-line, 6 received fourth-line, and 3 received sixth-line treatment with an EGFR inhibitor. Fourth- and sixth-line therapies were excluded due to the small patient size. There was no significant difference in estimated survival time between any of the lines. Median survival of the therapies was found. Conclusions. There is no statistical difference in survival when EGFR inhibitors are used as first-, second-, or third-line for stage 4 colon cancer which should be considered when prescribing chemotherapy second- or third-line for this cancer

Characteristics, staging and outcomes of differentiated thyroid cancer in patients with and without Graves’ disease

Background: The incidence of thyroid cancer has increased over the last three decades with studies showing incidence of thyroid cancer is higher among patients with Graves’ Disease (GD) when compared to Toxic multinodular goiter.1 We conducted a retrospective study to further investigate characteristics and outcomes in patients with thyroid cancer and GD. Methods: We retrospectively reviewed 62 patients with a diagnosis of Differentiated Thyroid Cancer (DTC). We compared age at diagnosis, type, size of tumor, radioactive iodine (RAI) use, and DTC recurrence amongst patients with GD, non-GD patients. We used Chi-square to test for independence among categorical variables at a nominal level of 0.05; comparison was based on t-test. Results: Out of 62 patients, 29 patients had GD and DTC (47%). 94% had papillary thyroid cancer. Patients with GD were diagnosed with DTC at a younger age (mean 46 years) in comparison to patients without GD (mean 53 years). There was no difference in the type of DTC. Patients with GD had significantly smaller tumor size (mean size 1.035 cm; p value = 0.002), more Stage 1 and 2 compared to patients without GD (p-value = 0.009). Both groups of patients had similar rates of recurrence on follow up and RAI use. Conclusion: We found patients with GD had smaller tumor size, early-stage DTC when compared to patients without GD and potentially favorable prognosis. More data is needed to understand whether this is due to pathogenesis like Graves antibodies promoting tumor formation or merely earlier detection of DTC in GD

Dataset for: Estimation of smooth ROC curves for biomarkers with limits of detection

Protein biomarkers found in plasma are commonly used for cancer screening and early detection. Measurements obtained by such markers are often based on different assays that may not support detection of accurate measurements due to a limit of detection (LOD). The $ROC$ curve is the most popular statistical tool for the evaluation of a continuous biomarker. However, in situations where LODs exist, the empirical $ROC$ curve fails to provide a valid estimate for the whole spectrum of the false positive rate (FPR). Hence, crucial information regarding the performance of the marker in high sensitivity and/or high specificity values is not revealed. In this paper, we address this problem and propose methods for constructing $ROC$ curve estimates for all possible $FPR$ values. We explore flexible parametric methods, transformations to normality, and robust kernel-based and spline-based approaches. We evaluate our methods though simulations and illustrate them in colorectal and pancreatic cancer data

Circulating microRNA Panel for Prediction of Recurrence and Survival in Early-Stage Lung Adenocarcinoma

Early-stage lung adenocarcinoma (LUAD) patients remain at substantial risk for recurrence and disease-related death, highlighting the unmet need of biomarkers for the assessment and identification of those in an early stage who would likely benefit from adjuvant chemotherapy. To identify circulating miRNAs useful for predicting recurrence in early-stage LUAD, we performed miRNA microarray analysis with pools of pretreatment plasma samples from patients with stage I LUAD who developed recurrence or remained recurrence-free during the follow-up period. Subsequent validation in 85 patients with stage I LUAD resulted in the development of a circulating miRNA panel comprising miR-23a-3p, miR-320c, and miR-125b-5p and yielding an area under the curve (AUC) of 0.776 in predicting recurrence. Furthermore, the three-miRNA panel yielded an AUC of 0.804, with a sensitivity of 45.8% at 95% specificity in the independent test set of 57 stage I and II LUAD patients. The miRNA panel score was a significant and independent factor for predicting disease-free survival (p p = 0.001, HR = 1.51, 95% CI = 1.17–1.94). This circulating miRNA panel is a useful noninvasive tool to stratify early-stage LUAD patients and determine an appropriate treatment plan with maximal efficacy

A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer

BACKGROUND: We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC. METHODS: A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated. RESULTS: Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19-9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02). CONCLUSIONS: A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone