The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

Tharmaseelan, Hishan; Hertel, Alexander; Rennebaum, Shereen; Nörenberg, Dominik; Haselmann, Verena; Schoenberg, Stefan O.; Froelich, Matthias F.

doi:10.3390/cancers14143349

Cited by 3 publications

(3 citation statements)

References 114 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it is of crucial importance to detect both the interlesional and the intralesional tumor heterogeneity and to adapt the targeted (possibly personalized) cancer therapy to it. Despite histopathological or blood-based approaches such as Liquid Profiling (LP), modern imaging modalities and quantitative image analysis are promising devices for detecting tumor heterogeneity 8 . Since heterogeneity in or between tumoral lesions is not yet sufficiently considered in the current clinical routine, there is a need for integration of these methods, which can recognize and take heterogeneity factors into account.…”

Section: Introductionmentioning

confidence: 99%

Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment

Haag,

Hertel,

Tharmaseelan

et al. 2023

Rofo

Self Cite

View full text Add to dashboard Cite

With personalized tumor therapy, understanding and addressing the heterogeneity of malignant tumors is becoming increasingly important. Heterogeneity can be found within one lesion (intralesional) and between several tumor lesions emerging from one primary tumor (interlesional). The heterogeneous tumor cells may show a different response to treatment due to their biology, which in turn influences the outcome of the affected patients and the choice of therapeutic agents. Therefore, both intra- and interlesional heterogeneity should be addressed at the diagnostic stage. While genetic and biological heterogeneity are important parameters in molecular tumor characterization and in histopathology, they are not yet addressed routinely in medical imaging. This article summarizes the recently established markers for tumor heterogeneity in imaging as well as heterogeneous/mixed response to therapy. Furthermore, a look at emerging markers is given. The ultimate goal of this overview is to provide comprehensive understanding of tumor heterogeneity and its implications for radiology and for communication with interdisciplinary teams in oncology. Key points:

show abstract

Section: Introductionmentioning

confidence: 99%

Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment

Haag,

Hertel,

Tharmaseelan

et al. 2023

Rofo

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, there has been a growing interest in employing radiomics-based AI models to identify imaging biomarkers for tumor characterization [21][22][23][24]. Radiomics, fundamentally, is a quantitative method that transforms imaging data into actionable clinical information.…”

Section: Patient Selection and Risk Stratificationmentioning

confidence: 99%

Theranostics and artificial intelligence: new frontiers in personalized medicine

Bilgin,

Burkett

et al. 2024

Theranostics

View full text Add to dashboard Cite

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

show abstract

“…Simultaneously, radiomics methods can extract texture features that the naked eye cannot recognize for quantitative analysis. Therefore, in this study, we As a relatively new technology, radiomics has become a novel method for quantifying various data in imaging images and assisting diagnosis and disease treatment 31 and has been widely utilized in the diagnosis and differential diagnosis of spinal diseases. Chee et al obtained 14 radiomics features from CT images of patients having benign and malignant vertebral fractures.…”

Section: Model Validation and Comparisonmentioning

confidence: 99%

A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors

Wu¹,

Wei²,

Li³

et al. 2022

IDR

View full text Add to dashboard Cite

Objective The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS). Methods A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort. t -test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis. Results A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877). Conclusion The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.

show abstract

The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

Cited by 3 publications

References 114 publications

Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment

Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment

Theranostics and artificial intelligence: new frontiers in personalized medicine

A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors

Contact Info

Product

Resources

About