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Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

ABSTRACT: Quantitative image features, also known as radiomic features, have shown potential for predicting treatment outcomes in several body sites. We quantitatively analyzed ¹⁸Fluorine-fluorodeoxyglucose (¹⁸F-FDG) Positron Emission Tomography (PET) uptake heterogeneity in the Metabolic Tumor Volume (MTV) of eighty cervical cancer patients to investigate the predictive performance of radiomic features for two treatment outcomes: the development of distant metastases (DM) and loco-regional recurrent disease (LRR). We aimed to fit the highest predictive features in multiple logistic regression models (MLRs). To generate such models, we applied backward feature selection method as part of Leave-One-Out Cross Validation (LOOCV) within a training set consisting of 70% of the original patient cohort. The trained MLRs were tested on an independent set consisted of 30% of the original cohort. We evaluated the performance of the final models using the Area under the Receiver Operator Characteristic Curve (AUC). Accordingly, six models demonstrated superior predictive performance for both outcomes (four for DM and two for LRR) when compared to both univariate-radiomic feature models and Standard Uptake Value (SUV) measurements. This demonstrated approach suggests that the ability of the pre-radiochemotherapy PET radiomics to stratify patient risk for DM and LRR could potentially guide management decisions such as adjuvant systemic therapy or radiation dose escalation.

SUBMITTER: Altazi BA

PROVIDER: S-EPMC7771366 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

Altazi Baderaldeen A BA Fernandez Daniel C DC Zhang Geoffrey G GG Hawkins Samuel S Naqvi Syeda M SM Kim Youngchul Y Hunt Dylan D Latifi Kujtim K Biagioli Matthew M Venkat Puja P Moros Eduardo G EG

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) 20180221

Quantitative image features, also known as radiomic features, have shown potential for predicting treatment outcomes in several body sites. We quantitatively analyzed <sup>18</sup>Fluorine-fluorodeoxyglucose (<sup>18</sup>F-FDG) Positron Emission Tomography (PET) uptake heterogeneity in the Metabolic Tumor Volume (MTV) of eighty cervical cancer patients to investigate the predictive performance of radiomic features for two treatment outcomes: the development of distant metastases (DM) and loco-r ...[more]

PMID: 29475772

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Project description:Background: A pressing challenge in treating non-small cell lung cancer (NSCLC) lies in the significant variability of patient responses to immunotherapy. This issue underscores the critical need for innovative predictive models that can navigate beyond conventional biomarkers to enhance patient outcomes, particularly by addressing immunotherapy resistance or responsiveness. Hypothesis: We hypothesize that an integrated multi-omics approach will uncover interactions within the NSCLC tumor immune microenvironment (TIME) and identify novel biomarkers that are predictive of individual immunotherapy responses, thus aiding in the development of a robust personalized treatment planning model. Objective: To develop a predictive model for NSCLC immunotherapy response/resistance by identifying new biomarkers using co-detection by indexing (CODEX) and Digital Spatial Profiling of whole transcriptome atlas (DSP-WTA). Methods: We utilized a multi-omics approach, combining CODEX for spatial mapping of protein expression at the single-cell level and DSP-WTA for comprehensive transcriptomic insights from the cell types identified by CODEX. This methodology facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first-line immunotherapy. Results: Our analysis identified three cell types, proliferating tumor cells, granulocytes, and vessels, that are associated with resistance to immunotherapy. The high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the training cohort (N = 33) and 1.8 (p = 0.05) in the validation cohort (N = 35). In the response cell type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells returned a HR of 0.4 (p = 0.019) in the training set and 0.49 (p = 0.036) in the validation set. Gene signatures related to these cell types also predicted outcomes with high accuracy. The resistant gene model, which included 8 genes associated with epithelial-mesenchymal transition (EMT) and cell migration showed a HR of 5.3 (p < 0.001) in the training set and 2.2 (p = 0.036) in the validation set. The response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the training set and 0.38 (p = 0.034) in the validation set. Conclusion: This research highlights the potential of a multi-omics strategy in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve prediction of response to treatment.

Project description:ObjectiveTo systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.DesignSystematic review.Data sourcesPubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.Study selection and data extractionStudies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures.Results14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68-0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis.ConclusionsTB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models.Trial registrationThe study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782).

Dataset Information

Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

Publications

Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

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