Impact of image reconstruction algorithms on histogram analysis of iodine concentration for assessing chemotherapy response in pancreatic ductal adenocarcinoma.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology, Gifu University, Gifu City, Japan.
- Department of Radiology, Gifu University, Gifu City, Japan. [email protected].
- Department of Radiology Services, Gifu University Hospital, Gifu City, Japan.
- Department of Food Hygiene and Control, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, Egypt.
Abstract
To evaluate the impact of image reconstruction algorithms on histogram analysis of iodine concentration (IC) derived from dual-energy CT (DECT) to assess response to first-line chemotherapy in patients with pancreatic ductal adenocarcinoma (PDAC). We retrospectively analyzed 41 patients with PDAC who underwent pancreatic protocol DECT during first-line chemotherapy between January 2021 and January 2024. Iodine-based material decomposition images at the pancreatic phase were reconstructed using hybrid-iterative reconstruction (Hybrid-IR) and deep-learning image reconstruction at medium- and high-intensity levels (DLIR-M and DLIR-H). A region of interest was placed on PDAC, and histogram parameters of tumor IC were extracted from all three reconstructed image sets. These parameters were compared between the response (complete response [CR], partial response [PR], and stable disease [SD]) and non-response (progressive disease [PD]) groups. Receiver-operating-characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of significant histogram parameters for differentiating the response and non-response groups. The response and non-response groups were found to differ significantly in standard deviation, energy, and entropy of the Hybrid-IR (P < .001 for all); standard deviation (P = .002), energy (P < .001), and entropy (P < .001) of the DLIR-M; and standard deviation (P = .003), energy (P < .001), entropy (P < .001), and kurtosis (P = .01) of the DLIR-H. Among these 10 parameters, the entropy of the Hybrid-IR (area under the ROC curve, 0.94) and DLIR-M (0.91) demonstrated high and comparable diagnostic performance for differentiating the two groups, with no statistical difference (P = .20), while both outperformed DLIR-H (0.85) (P = .02-.04). The entropy of IC reconstructed with either Hybrid-IR or DLIR-M may serve as an imaging biomarker for assessing chemotherapy response in patients with PDAC. In contrast, DLIR-H may reduce diagnostically relevant texture information and should be used with caution for histogram-based tumor assessment.