scholarly journals Explainable Transformer-Based Neural Network forthe Prediction of Survival Outcomes in Non-SmallCell Lung Cancer (NSCLC)

2021 ◽  
Author(s):  
Gustavo Arango ◽  
Elly Kipkogei ◽  
Etai Jacob ◽  
Ioannis Kagiampakis ◽  
Arijit Patra
Keyword(s):  
Immune System ◽  
Precision Medicine ◽  
Cancer Patients ◽  
Patient Survival ◽  
Response To Therapy ◽  
Survival Models ◽  
Attractive Alternative ◽  
Survival Prediction ◽  
Molecular Features ◽  
Clinical Measurements

In this paper, we introduce the Clinical Transformer - a recasting of the widely used transformer architecture as a method for precision medicine to model relations between molecular and clinical measurements, and the survival of cancer patients. Although the emergence of immunotherapy offers a new hope for cancer patients with dramatic and durable responses having been reported, only a subset of patients demonstrate benefit. Such treatments do not directly target the tumor but recruit the patient immune system to fight the disease. Therefore, the response to therapy is more complicated to understand as it is affected by the patients physical condition, immune system fitness and the tumor. As in text, where the semantics of a word is dependent on the context of the sentence it belongs to, in immuno-therapy a biomarker may have limited meaning if measured independent of other clinical or molecular features. Hence, we hypothesize that the transformer-inspired model may potentially enable effective modelling of the semantics of different biomarkers with respect to patient survival time. Herein, we demonstrate that this approach can offer an attractive alternative to the survival models utilized incurrent practices as follows: (1) We formulate an embedding strategy applied to molecular and clinical data obtained from the patients. (2) We propose a customized objective function to predict patient survival. (3) We show the applicability of our proposed method to bioinformatics and precision medicine. Applying the clinical transformer to several immuno-oncology clinical studies, we demonstrate how the clinical transformer outperforms other linear and non-linear methods used in current practice for survival prediction. We also show that when initializing the weights of a domain-specific transformer by the weights of a cross-domain transformer, we further improve the predictions. Lastly, we show how the attention mechanism successfully captures some of the known biology behind these therapies

scholarly journals Patient-Derived Xenograft and Organoid Models for Precision Medicine Targeting of the Tumour Microenvironment in Head and Neck Cancer

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3743
Author(s):  
Tet Woo Lee ◽  
Amy Lai ◽  
Julia K. Harms ◽  
Dean C. Singleton ◽  
Benjamin D. Dickson ◽  
...  
Keyword(s):  
Head And Neck ◽  
Precision Medicine ◽  
Patient Survival ◽  
Predictive Biomarkers ◽  
Tumour Microenvironment ◽  
Response To Therapy ◽  
Preclinical Models ◽  
Therapeutic Success ◽  
Patient Derived Xenograft ◽  
Individualise Therapy

Patient survival from head and neck squamous cell carcinoma (HNSCC), the seventh most common cause of cancer, has not markedly improved in recent years despite the approval of targeted therapies and immunotherapy agents. Precision medicine approaches that seek to individualise therapy through the use of predictive biomarkers and stratification strategies offer opportunities to improve therapeutic success in HNSCC. To enable precision medicine of HNSCC, an understanding of the microenvironment that influences tumour growth and response to therapy is required alongside research tools that recapitulate the features of human tumours. In this review, we highlight the importance of the tumour microenvironment in HNSCC, with a focus on tumour hypoxia, and discuss the fidelity of patient-derived xenograft and organoids for modelling human HNSCC and response to therapy. We describe the benefits of patient-derived models over alternative preclinical models and their limitations in clinical relevance and how these impact their utility in precision medicine in HNSCC for the discovery of new therapeutic agents, as well as predictive biomarkers to identify patients’ most likely to respond to therapy.

Development of an artificial intelligence model to predict survival at specific time intervals for lung cancer patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6556-6556 ◽  
Author(s):  
Smita Agrawal ◽  
Vivek Vaidya ◽  
Prajwal Chandrashekaraiah ◽  
Hemant Kulkarni ◽  
Li Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Prediction Models ◽  
Parameter Tuning ◽  
Machine Learning Techniques ◽  
Survival Models ◽  
Gradient Boosting ◽  
Survival Prediction ◽  
Lung Cancer Patients ◽  
Time Points

6556 Background: Survival prediction models for lung cancer patients could help guide their care and therapy decisions. The objectives of this study were to predict probability of survival beyond 90, 180 and 360 days from any point in a lung cancer patient’s journey. Methods: We developed a Gradient Boosting model (XGBoost) using data from 55k lung cancer patients in the ASCO CancerLinQ database that used 3958 unique variables including Dx and Rx codes, biomarkers, surgeries and lab tests from ≤1 year prior to the prediction point, which was chosen at random for each patient. We used 40% data for training, 25% for hyper-parameter tuning, 20% for testing and 15% for holdout validation. Death date available in the Electronic Health Record was cross checked by linkage to death registries. Results: The model was validated on the holdout set of 8,468 patients. The Area Under the Curve (AUC) for the model was 0.79. The precision and recall for predicting survival beyond the three time points were between 0.7-0.8 and 0.8-0.9 respectively (see table). This compares favourably to other lung cancer survival models created using different machine learning techniques (Jochems 2017, Dekker 2009). A Cox-PH model created using the top 20 variables also had a significantly lower performance (see table). Analysis of input variables yielded distinctive patterns for patient subgroups and time points. Tumor status, medications, lab values and functional status were found to be significant in patient sub cohorts. Conclusions: An AI model to predict survival of lung cancer patients built using a large real world dataset yielded high accuracy. This general model can further be used to predict survival of sub cohorts stratified by variables such as stage or various treatment effects. Such a model could be useful for assessing patient risk and treatment options, evaluating cost and quality of care or determining clinical trial eligibility. [Table: see text]

scholarly journals Breast cancer stem-like cells: Clinical implications and therapeutic strategies

2016 ◽  
Vol 89 (2) ◽  
pp. 193-198 ◽  
Author(s):  
Oana Mihaela Tudoran ◽  
Ovidiu Balacescu ◽  
Ioana Berindan-Neagoe
Keyword(s):  
Breast Cancer ◽  
Stem Cells ◽  
Cancer Patients ◽  
Life Quality ◽  
Treatment Decision ◽  
Tumor Formation ◽  
Response To Therapy ◽  
Breast Cancer Patients ◽  
Female Population ◽  
Molecular Features

Breast cancer is the most frequently diagnosed can­cer in women, being also the leading cause of cancer death among female population, including in Romania. Resistance to therapy represents a major problem for cancer treatment. Current cancer treatments are both expensive and induce serious side effects; therefore ineffective therapies are both traumatic and pricy. Characterizing predictive markers that can identify high-risk patients could contribute to dedicated/personalized therapy to improve the life quality and expectancy of cancer patients. Moreover, there are some markers that govern specific tumor molecular features that can be targeted with specific therapies for those patients who are most likely to benefit. The identification of stem cells in both normal and malignant breast tissue have lead to the hypothesis that breast tumors arise from breast cancer stem-like cells (CSCs), and that these cells influence tumor’s response to therapy. CSCs have similar self-renewal properties to normal stem cells, however the balance between the signaling pathways is altered towards tumor formation In this review, we discuss the molecular aspects of breast CSCs and the controversies regarding their use in the diagnosis and treatment decision of breast cancer patients.

Cancer Biomarker Discovery for Precision Medicine: New Progress

2020 ◽  
Vol 26 (42) ◽  
pp. 7655-7671 ◽  
Author(s):  
Jinfeng Zou ◽  
Edwin Wang
Keyword(s):  
Precision Medicine ◽  
Cancer Patients ◽  
Clinical Application ◽  
Liquid Biopsy ◽  
Biomarker Discovery ◽  
Deep Understanding ◽  
Genetic Mutation ◽  
Data Driven ◽  
Novel Approaches ◽  
Functional Biomarkers

Background: Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued. Objective: This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine. Results: The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients’ survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients’ survival. Conclusion: Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.

scholarly journals The patients’ perspective on coordinated action for cancer and AMR

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
K Apostolidis
Keyword(s):  
Quality Of Life ◽  
Immune System ◽  
Cancer Patients ◽  
Cancer Treatments ◽  
Unique Challenge ◽  
Coordinated Action ◽  
The Impact ◽  
Patients Perspective

Abstract The speaker will present the perspective of the cancer patients, and the challenges they encounter across the spectrum of care and what measures they consider relevant in terms of prevention, diagnosis, treatment and, indeed, to raise awareness of the impact of AMR on rendering cancer treatments ineffective. She will elaborate on survivorship, and on the impact of AMR on the quality of life of patients, their carers, and families. Emphasis will be given on the implications of modern therapies, such as immunotherapy, representing a unique challenge in terms of better understanding the effect on overall health of patients, with the effect they have the immune system, further weakening the patient and leaving him/her exposed to infections potentially of higher risk than cancer itself.

scholarly journals Association of chemotherapy with survival in stage II colon cancer patients who received radical surgery: a retrospective cohort study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhihao Lv ◽  
Yuqi Liang ◽  
Huaxi Liu ◽  
Delong Mo
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Prediction Models ◽  
Radical Surgery ◽  
Survival Rates ◽  
Stage Ii ◽  
Survival Prediction ◽  
Stage Ii Colon Cancer ◽  
Overall Survival Rates

Abstract Background It remains controversial whether patients with Stage II colon cancer would benefit from chemotherapy after radical surgery. This study aims to assess the real effectiveness of chemotherapy in patients with stage II colon cancer undergoing radical surgery and to construct survival prediction models to predict the survival benefits of chemotherapy. Methods Data for stage II colon cancer patients with radical surgery were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (1:1) was performed according to receive or not receive chemotherapy. Competitive risk regression models were used to assess colon cancer cause-specific death (CSD) and non-colon cancer cause-specific death (NCSD). Survival prediction nomograms were constructed to predict overall survival (OS) and colon cancer cause-specific survival (CSS). The predictive abilities of the constructed models were evaluated by the concordance indexes (C-indexes) and calibration curves. Results A total of 25,110 patients were identified, 21.7% received chemotherapy, and 78.3% were without chemotherapy. A total of 10,916 patients were extracted after propensity score matching. The estimated 3-year overall survival rates of chemotherapy were 0.7% higher than non- chemotherapy. The estimated 5-year and 10-year overall survival rates of non-chemotherapy were 1.3 and 2.1% higher than chemotherapy, respectively. Survival prediction models showed good discrimination (the C-indexes between 0.582 and 0.757) and excellent calibration. Conclusions Chemotherapy improves the short-term (43 months) survival benefit of stage II colon cancer patients who received radical surgery. Survival prediction models can be used to predict OS and CSS of patients receiving chemotherapy as well as OS and CSS of patients not receiving chemotherapy and to make individualized treatment recommendations for stage II colon cancer patients who received radical surgery.

scholarly journals Dynamic changes in practical inflammation and immunity markers in cancer patients receiving immune-enhancing nutritional supplementation during concurrent chemoradiotherapy

2021 ◽  
pp. 1-11
Author(s):  
Nontiya Homkham ◽  
Pooriwat Muangwong ◽  
Veeradej Pisprasert ◽  
Patrinee Traisathit ◽  
Rungarun Jiratrachu ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Immune System ◽  
Cancer Patients ◽  
Concurrent Chemoradiotherapy ◽  
Absolute Lymphocyte Count ◽  
Diet Group ◽  
Cancer Therapies ◽  
Dynamic Changes ◽  
Regular Diet ◽  
Over Time

BACKGROUND: Immune-enhancing nutrition (IMN) strengthens the systematic inflammatory response and the immune system. Neutrophil to lymphocyte ratio (NLR) and absolute lymphocyte count (ALC) are affected during cancer therapies. OBJECTIVE: We carried out an analysis of the dynamic changes in NLR and ALC over time in cancer patients with or without IMN supplementation. METHODS: 88 cancer patients receiving concurrent chemoradiotherapy (CCRT) were randomized into regular diet group, and regular diet and IMN group.Generalized estimation equation models were used to assess associations between patient’s characteristics, IMN, and dynamic changes in NLR and ALC over time. RESULTS: NLR and ALC at preCCRT were significantly associated with dynamic changes in NLR (adjusted β= 1.08, 95% confidence interval [CI]: 0.64–1.52) and ALC (adjusted β= 0.41, 95% CI: 0.36–0.46). The magnitudes of the NLR and ALC changes through CCRT were lower in patients receiving IMN, although the differences were not statistically significant except ALC at the end of CCRT in head and neck cancer patients (P= 0.023). CONCLUSION: Dynamic negative changes in both markers were demonstrated throughout CCRT. There were non-significant trend in promising changes in both NLR and ALC values in the whole group in IMN supplementation.

scholarly journals Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 956
Author(s):  
Marcello Andrea Tipaldi ◽  
Edoardo Ronconi ◽  
Elena Lucertini ◽  
Miltiadis Krokidis ◽  
Marta Zerunian ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Clinical Data ◽  
Transarterial Chemoembolization ◽  
Prognostic Value ◽  
Texture Features ◽  
Response To Therapy ◽  
Survival Prediction ◽  
Ct Examination ◽  
Drug Eluting ◽  
Pre Treatment

(1) Introduction and Aim: The aim of this study is to investigate the prognostic value, in terms of response and survival, of CT-based radiomics features for patients with HCC undergoing drug-eluting beads transarterial chemoembolization (DEB-TACE). (2) Materials and Methods: Pre-treatment CT examinations of 50 patients with HCC, treated with DEB-TACE were manually segmented to obtain the tumor volumetric region of interest, extracting radiomics features with TexRAD. Response to therapy evaluation was performed basing on post-procedural CT examination compared to pre-procedural CT, using modified RECIST criteria for HCC. The prognostic value of texture analysis was evaluated, investigating the correlation between radiomics features, response to therapy and overall survival. Three models based on texture and clinical variables and a combination of them were finally built; (3) Results: Entropy, skewness, MPP and kurtosis showed a significant correlation with complete response (CR) to TACE (all p < 0.001). A predictive model to identify patients with a high and low probability of CR was evaluated with an ROC curve, with an AUC of 0.733 (p < 0.001). The three models built for survival prediction yielded an HR of 2.19 (95% CI: 2.03–2.35) using texture features, of 1.7 (95% CI: 1.54–1.9) using clinical data and of 4.61 (95% CI: 4.24–5.01) combining both radiomics and clinical data (all p < 0.0001). (4) Conclusion: Texture analysis based on pre-treatment CT examination is associated with response to therapy and survival in patients with HCC undergoing DEB-TACE, especially if combined with clinical data.

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