Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer

• Verfasst von: Heidt, Christian [VerfasserIn]
• Verfasst von: Bohn, Jonas [VerfasserIn]
• Verfasst von: Stollmayer, Róbert [VerfasserIn]
• Verfasst von: Stackelberg, Oyunbileg von [VerfasserIn]
• Verfasst von: Rheinheimer, Stephan [VerfasserIn]
• Verfasst von: Bozorgmehr, Farastuk [VerfasserIn]
• Verfasst von: Senghas, Karsten [VerfasserIn]
• Verfasst von: Schlamp, Kai [VerfasserIn]
• Verfasst von: Weinheimer, Oliver [VerfasserIn]
• Verfasst von: Giesel, Frederik L. [VerfasserIn]
• Verfasst von: Kauczor, Hans-Ulrich [VerfasserIn]
• Verfasst von: Heußel, Claus Peter [VerfasserIn]
• Verfasst von: Heußel, Gudula [VerfasserIn]
• Titel: Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer
• Verf.angabe: Christian M. Heidt, Jonas R. Bohn, Róbert Stollmayer, Oyunbileg von Stackelberg, Stephan Rheinheimer, Farastuk Bozorgmehr, Karsten Senghas, Kai Schlamp, Oliver Weinheimer, Frederik L. Giesel, Hans-Ulrich Kauczor, Claus Peter Heußel and Gudula Heußel
• E-Jahr: 2024
• Jahr: 26 August 2024
• Umfang: 11 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 16.10.2024
• Titel Quelle: Enthalten in: Insights into imaging
• Ort Quelle: Berlin : Springer, 2010
• Jahr Quelle: 2024
• Band/Heft Quelle: 15(2024), Artikel-ID 218, Seite 1-11
• ISSN Quelle: 1869-4101
• DOI: doi:10.1186/s13244-024-01787-5
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Diffusion-weighted MRI
• Sach-SW: Lung cancer
• Sach-SW: Non-small cell lung cancer
• Sach-SW: Radiomics
• Sach-SW: Tyrosine kinase inhibitors
• K10plus-PPN: 1905865457

Abstract

Objective: Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features. Methods: This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS). Results: Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival. Conclusion: Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early. Critical relevance statement: Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging.