More than Meets the Eye: Integration of Radiomics with Transcriptomics for Reconstructing the Tumor Microenvironment and Predicting Response to Therapy

Logotheti, Stella; Georgakilas, Alexandros G.

doi:10.3390/cancers15061634

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Transcriptomics, genomics, proteomics, metabolomics, and glycomics are some of the '-omics' data that are converging to identify critical bioindicators and biomarkers that can be used to train models. These can be further combined with radiomics to identify populations that could be radiosensitive [186][187][188]. For example, Zhang et al combined gene expression, DNA methylation, and clinical data to identify eight radiosensitivityrelated genes (AR, WBP1, AKR1E2, FANCG, NR2C2AP, CXCR4, SYNE4, and WFDC2) [189], while Liu et al identified 12 genes (BEST2, TMPRSS15, FGF19, ALP1, KCNB2, CLDN6, IL17REL, RORB, DDX25, TDRD9, CELF3, and FABP7) that can aid in identifying population responses in head and neck cancer patients [190].…”

Section: Challenges and Opportunities For Cancer Radiosensitivity Bio...mentioning

confidence: 99%

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Gkikoudi,

Kalospyros,

Triantopoulou

et al. 2023

Applied Sciences

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Radiotherapy (RT) is a major part of cancer treatment. The reported variability in patient response to this modality can interfere with the continuation of best-possible care, promote side effects, and lead to long-term morbidity. Tools to predict a patient’s response to radiation could be highly useful in improving therapeutic outcomes while minimizing unnecessary and toxic exposure to radiation. This study investigates the potential of using molecular biomarkers as predictors of radiosensitivity in clinical practice. We review relative studies researching the positive correlation between various molecular biomarkers and patient radiosensitivity, including DNA damage response and repair proteins, inflammation and apoptosis markers, cell cycle regulators, and other biological markers. The clinical perspectives and applicability of these biomarkers in the prediction of radiosensitivity are also critically discussed. Conclusively, we underline the dynamics of molecular biomarkers to improve the efficacy and safety of radiotherapy in clinical practice and highlight the need for further research in this field. Identification of the most prominent markers is crucial for the personalization of therapies entailing ionizing radiation.

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Section: Challenges and Opportunities For Cancer Radiosensitivity Bio...mentioning

confidence: 99%

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Gkikoudi,

Kalospyros,

Triantopoulou

et al. 2023

Applied Sciences

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“…This approach enables the conversion of qualitative information, based on medical doctors' experience, into objective information. In other words, by analyzing quantitative features, radiomics aims to uncover valuable information that may not be discernible through traditional visual inspection alone [32]. The radiomics features can be extracted starting from the original images or after performing the wavelet filter, i.e., the images are first decomposed according by the SWT and then the features are calculated on each part [33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images

Corso,

Stefano,

Salvaggio

et al. 2024

Mathematics

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For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. A recent application of wavelet theory is in radiomics, an emerging field aiming to improve diagnostic, prognostic and predictive analysis of various cancer types through the analysis of features extracted from medical images. In this paper, for a radiomics study of prostate cancer with magnetic resonance (MR) images, we apply a similar but more sophisticated tool, namely the shearlet transform which, in contrast to the wavelet transform, allows us to examine variations along more orientations. In particular, we conduct a parallel radiomics analysis based on the two different transformations and highlight a better performance (evaluated in terms of statistical measures) in the use of the shearlet transform (in absolute value). The results achieved suggest taking the shearlet transform into consideration for radiomics studies in other contexts.

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Advancing Rheumatology Care Through Machine Learning

Hügle

2024

Pharm Med

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Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring. Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity. Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms. In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.

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More than Meets the Eye: Integration of Radiomics with Transcriptomics for Reconstructing the Tumor Microenvironment and Predicting Response to Therapy

Abstract: For over a decade, large cancer-related datasets (big data) have continuously been produced and made publicly available to the scientific community [...]

Cited by 3 publications

References 18 publications

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images

Advancing Rheumatology Care Through Machine Learning

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