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. 2024 Aug 26;15(1):218.
doi: 10.1186/s13244-024-01787-5.

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

Affiliations

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

Christian M Heidt et al. Insights Imaging. .

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.

Key points: Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

Keywords: Diffusion-weighted MRI; Lung cancer; Non-small cell lung cancer; Radiomics; Tyrosine kinase inhibitors.

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Conflict of interest statement

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Editorial board members: C.P.H.: Med Klin Intensivmed Notfmed, H.U.K.: Medical Radiology and Diagnostic Imaging; F.L.G.: J Radiol Oncol, J Nucl Med, J Radiol Radiat Ther, and Sci Rep.

Figures

Fig. 1
Fig. 1
Flowchart of the study. TKI, tyrosine-kinase inhibitors; PBC, platinum-based chemotherapy
Fig. 2
Fig. 2
Hierarchical clustering heatmap of changes in feature expression at FU2. Rows signify patients, columns signify features. The color coding next to the heatmap indicates RECIST classification at the first follow-up CT (red: PD, blue: SD, and green: PR). Well visible are two distinct groups of patients, one with decreased expression of the first 6 features and an increase of the remaining, the second with the reversed pattern. The horizontal black bar defines the separation between cluster 1 (top) and cluster 2 (bottom)
Fig. 3
Fig. 3
Kaplan–Meier estimators illustrating (a) PFS functions, (b) OS functions between the identified patient clusters from Fig. 2. Cluster 2 shows prolonged survival for both metrics as opposed to cluster 1, with PFS being significantly prolonged at p = 0.01 and OS showing a similar tendency at p = 0.06 after applying log-rank test. The OS function contains right-censored data of 22 patients lost to follow-up
Fig. 4
Fig. 4
Expression changes between BL and follow-up imaging per group. G_IDN, gradient inverse difference normalized; SRE, short-run emphasis; G_SRE, gradient SRE; ZP, zone percentage; FU1, follow-up 1; FU2, follow-up 2. *p < 0.05, **p < 0.01
Fig. 5
Fig. 5
ADC maps and feature maps showing differential expression of feature “ShortRunEmphasis” (SRE) on BL and FU2 imaging (brighter colors mean higher expression). a Patient with SD, TKI group (progression: 1039 days). Visible is an increase in SRE as a brightening especially in the center of the ROI; a similar effect is visible in (c) patient with PR, TKI (progression: 419 days). Opposed are (b) patient with PD, TKI group (progression: 56 days) with a visible decrease in SRE and (d) patient with PD, PBC group (progression: 40 days)

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