Quantitative computed tomography imaging biomarkers in the diagnosis and management of lung cancer
Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2015
|
| In: |
Investigative radiology
Year: 2015, Jahrgang: 50, Heft: 9, Pages: 571-583 |
| ISSN: | 1536-0210 |
| DOI: | 10.1097/RLI.0000000000000152 |
| Online-Zugang: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1097/RLI.0000000000000152 Verlag, lizenzpflichtig, Volltext: https://journals.lww.com/investigativeradiology/Fulltext/2015/09000/Quantitative_Computed_Tomography_Imaging.4.aspx 
|
| Verfasserangaben: | Hyungjin Kim, Chang Min Park, Jin Mo Goo, Joachim E. Wildberger, and Hans-Ulrich Kauczor |
| Zusammenfassung: | Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers such as tumor volume and mass, ground-glass opacities, perfusion parameters, as well as texture features in this review. Herein, we first appraised the conventional 1- or 2-dimensional measurement with brief discussion on their limits and then introduced the potential imaging biomarkers with emphasis on the current understanding of their clinical usefulness with respect to the malignancy differentiation, treatment response monitoring, and patient outcome prediction. |
|---|---|
| Beschreibung: | Gesehen am 01.07.2020 |
| Beschreibung: | Online Resource |
| ISSN: | 1536-0210 |
| DOI: | 10.1097/RLI.0000000000000152 |