Personalized Decision Modeling for Intervention and Prevention of Cancers

Personalized medicine has been utilized in all stages of cancer care in recent years, including the prevention, diagnosis, treatment and follow-up. Since prevention and early intervention are particularly crucial in reducing cancer mortalities, personalizing the corresponding strategies and decisions so as to provide the most appropriate or optimal medical services for different patients can greatly improve the current cancer control practices. This dissertation research performs an in-depth exploration of personalized decision modeling of cancer intervention and prevention problems. We investigate the patient-specific screening and vaccination strategies for breast cancer and the cancers related to human papillomavirus (HPV), representatively. Three popular healthcare analytics techniques, Markov models, regression-based predictive models, and discrete-event simulation, are developed in the context of personalized cancer medicine. We discuss multiple possibilities of incorporating patient-specific risk into personalized cancer prevention strategies and showcase three practical examples. The first study builds a Markov decision process model to optimize biopsy referral decisions for women who receives abnormal breast cancer screening results. The second study directly optimizes the annual breast cancer screening using a regression-based adaptive decision model. The study also proposes a novel model selection method for logistic regression with a large number of candidate variables. The third study addresses the personalized HPV vaccination strategies and develops a hybrid model combining discrete-event simulation with regression-based risk estimation. Our findings suggest that personalized screening and vaccination benefit patients by maximizing life expectancies and minimizing the possibilities of dying from cancer. Preventive screening and vaccination programs for other cancers or diseases, which have clearly identified risk factors and measurable risk, may all benefit from patient-specific policies

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Locoregional stage assessment in clinically node negative breast cancer: Clinical, imaging, pathologic, and statistical methods

The locoregional staging remains an essential part of prognostication in breast cancer. Tumour size and biology, together with the number of lymph node metastases, guide the planning of appropriate treatments. Accurate clinical, imaging, pathologic, and statistical staging is needed as the surgical staging diminishes. In this study, 743 clinically lymph node negative breast cancer patients treated in 2009‒2017 were evaluated. Clinopathological factors were investigated in association with the number of lymph node metastases, the use of preoperative imaging methods and the surgical treatment method. A nomogram was developed and tested to predict the number of lymph node metastases after sentinel lymph node positivity. Three previously published models were validated to confirm their feasibility in the current population to predict nodal stage pN2a or pN3a. Tumour size, biologic subtype and proliferation associated with higher numbers of lymph node metastases. To predict stage pN2a or pN3a, the machine learning algorithms identified tumour size, invasive ductal histology, multifocality, lymphovascular invasion, oestrogen receptor status and the number of positive sentinel lymph nodes as risk factors. The nomograms performed well with favourable discrimination. Clinopathological factors seemed to guide preoperative magnetic resonance imaging (MRI) prior to more extensive surgery. MRI estimated the increasing tumour size more accurately than mammography or ultrasound. According to this study, clinopathological factors, additional preoperative MRI and modern statistics can be utilized in breast cancer staging without extensive surgical interference. The importance of non-surgical investigations in staging is growing in the planning of surgical, systemic and radiation treatments. Thus, maintaining the impressive survival outcomes of clinically node negative breast cancer patients can be achieved.Kliinisesti imusolmukenegatiivisen rintasyövän paikallislevinneisyyden arvioiminen. Kliiniset, kuvantamisen, patologian alan ja tilastotieteen menetelmät Kasvaimen paikallinen levinneisyys on tärkeä rintasyövän ennustetekijä. Kasvaimen koko ja biologia sekä imusolmukemetastaasien lukumäärä ohjaavat syöpähoitojen suunnittelua. Levinneisyyden selvittelyssä tarvitaan huolellista kliinistä tutkimusta sekä tarkkoja kuvantamisen, patologian alan ja tilastotieteen menetelmiä, kun kirurginen levinneisyysluokittelu vähenee. Tutkimuksessa arvioitiin vuosina 2009‒2017 hoidettujen 743 kliinisesti imusolmukenegatiivisen suomalaisen potilaan tietoja. Työssä selvitettiin kliinispatologisten tekijöiden ja kainaloimusolmukemetastaasien lukumäärän, leikkausta edeltävien kuvantamistutkimusten sekä leikkausmenetelmien yhteyttä. Ennustemalli kehitettiin ja koekäytettiin positiivisen vartijaimusolmuketutkimuksen jälkeisen imusolmukemetastaasien määrän arvioimiseksi. Kolme aiemmin julkaistua mallia validoitiin, jotta niiden käyttökelpoisuus imusolmukeluokan pN2a tai pN3a ennustamisessa varmistuisi tässä aineistossa. Kasvainkoko, biologinen alatyyppi ja jakautumisnopeus olivat yhteydessä suurempaan imusolmukemetastaasien määrään. Koneoppimisalgoritmit määrittivät levinneisyysluokan pN2a tai pN3a ennustamiseksi tarvittaviksi tekijöiksi kasvainkoon, invasiivisen duktaalisen histologian, monipesäkkeisyyden, suoni-invaasion, estrogeenireseptoristatuksen sekä positiivisten vartijaimusolmukkeiden määrän. Ennustemallit toimivat aineistossa hyvin osoittaen suotuisaa erotuskykyä. Kliinispatologiset tekijät näyttivät ohjaavan magneettikuvauspäätöstä ennen laajaa kirurgista hoitoa. Magneettikuvaus oli tarkin kuvantamismenetelmä suurenevan kasvainkoon arvioinnissa. Tämän tutkimuksen perusteella kliinispatologiset tekijät, leikkausta edeltävä täydentävä magneettikuvaus ja nykyaikaiset tilastotieteen menetelmät voivat hyödyttää rintasyövän levinneisyysluokittelua ilman laajoja kirurgisia toimenpiteitä. Kajoamattomien tutkimusten asema levinneisyysluokittelussa on vahvistumassa kirurgisten, lääkkeellisten ja sädehoitojen suunnittelun yhteydessä. Tarkka levinneisyysluokittelu edesauttaa kliinisesti imusolmukenegatiivisten rintasyöpäpotilaiden erinomaista ennustetta

Risk assessment and prevention of breast cancer

One woman in eight develops breast cancer during her lifetime in the Western world. Measures are warranted to reduce mortality and to prevent breast cancer. Mammography screening reduces mortality by early detection. However, approximately one fourth of the women who develop breast cancer are diagnosed within two years after a negative screen. There is a need to identify the short-term risk of these women to better guide clinical followup. Another drawback of mammography screening is that it focuses on early detection only and not on breast cancer prevention. Today, it is known that women attending screening can be stratified into high and low risk of breast cancer. Women at high risk could be offered preventive measures such as low-dose tamoxifen to reduce breast cancer incidence. Women at low risk do not benefit from screening and could be offered less frequent screening. In study I, we developed and validated the mammographic density measurement tool STRATUS to enable mammogram resources at hospitals for large scale epidemiological studies on risk, masking, and therapy response in relation to breast cancer. STRATUS showed similar measurement results on different types of mammograms at different hospitals. Longitudinal studies on mammographic density could also be analysed more accurate with less nonbiological variability. In study II, we developed and validated a short-term risk model based on mammographic features (mammographic density, microcalcifications, masses) and differences in occurrences of mammographic features between left and right breasts. The model could optionally be expanded with lifestyle factors, family history of breast cancer, and genetic determinants. Based on the results, we showed that among women with a negative mammography screen, the short-term risk tool was suitable to identify women that developed breast cancer before or at next screening. We also showed that traditional long-term risk models were less suitable to identify the women who in a short time-period after risk assessment were diagnosed with breast cancer. In study III, we performed a phase II trial to identify the lowest dose of tamoxifen that could reduce mammographic density, an early marker for reduced breast cancer risk, to the same extent as standard 20 mg dose but cause less side-effects. We identified 2.5 mg tamoxifen to be non-inferior for reducing mammographic density. The women who used 2.5 mg tamoxifen also reported approximately 50% less severe vasomotor side-effects. In study IV, we investigated the use of low-dose tamoxifen for an additional clinical use case to increase screening sensitivity through its effect on reducing mammographic density. It was shown that 24% of the interval cancers have a potential to be detected at prior screen. In conclusion, tools were developed for assessing mammographic density and breast cancer risk. In addition, two low-dose tamoxifen concepts were developed for breast cancer prevention and improved screening sensitivity. Clinical prospective validation is further needed for the risk assessment tool and the low-dose tamoxifen concepts for the use in breast cancer prevention and for reducing breast cancer mortality

Deep Functional Mapping For Predicting Cancer Outcome

The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network. In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents

Blood-based liquid biopsies for prostate cancer: clinical opportunities and challenges

Liquid biopsy has been established as a powerful, minimally invasive, tool to detect clinically actionable aberrations across numerous cancer types in real-time. With the development of new therapeutic agents in prostate cancer (PC) including DNA repair targeted therapies, this is especially attractive. However, there is unclarity on how best to screen for PC, improve risk stratification and ultimately how to treat advanced disease. Therefore, there is an urgent need to develop better biomarkers to help guide oncologists’ decisions in these settings. Circulating tumour cells (CTCs), exosomes and cell-free DNA/RNA (cfDNA/cfRNA) analysis, including epigenetic features such as methylation, have all shown potential in prognostication, treatment response assessment and detection of emerging mechanisms of resistance. However, there are still challenges to overcome prior to implementing liquid biopsies in routine clinical practice such as preanalytical considerations including blood collection and storage, the cost of CTC isolation and enrichment, low-circulating tumour content as a limitation for genomic analysis and how to better interpret the sequencing data generated. In this review, we describe an overview of the up-to-date clinical opportunities in the management of PC through blood-based liquid biopsies and the next steps for its implementation in personalised treatment guidance

Supervised group Lasso with applications to microarray data analysis

BACKGROUND: A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure. RESULTS: We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data. CONCLUSION: We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods

Improving The Diagnosis And Risk Stratification Of Prostate Cancer

The current diagnostic and stratification pathway for prostate cancer has led to over-diagnosis and over- treatment. This thesis aims to improve the prostate cancer diagnosis pathway by developing a minimally invasive blood test to inform diagnosis alongside mpMRI and to understand the true Gleason 4 burden which will help better stratify disease and guide clinicians in treatment planning. To reduce the number of patients who have to undergo prostate biopsy after indeterminate or false positive prostate mpMRI, we aimed to develop a new panel of mRNA detectable in blood or urine that was able to improve the detection of clinical significant prostate cancer (Gleason 4+3 or ≥6mm) in combination with prostate mpMRI. mRNA expression of 28 potential genes was studied in four prostate cancer cell lines and, using publicly available datasets, a new seven gene biomarker panel was developed using machine learning techniques. The signature was then validated in blood and urine samples from the ProMPT, PROMIS and INNOVATE trials. To redefine the classification of Gleason 4 disease in prostate cancer patients, digital pathology was used to contour and accurately assess the burden and spread of Gleason 4 in a cohort of PROMIS patients compared to the gold standard manual pathology. There was a significant difference between observed and objective Gleason 4 burden that has implications in patient risk stratification and biomarker discovery. The work presented in this thesis makes a significant step toward improving the patient diagnostic and risk classification pathways by ensuring only the right patients are biopsied when necessary, improving the current pathological reference standard