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Purpose <p>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).</p> Methods <p>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.</p> Results <p>The response and non-response groups were found to differ significantly in standard deviation, energy, and entropy of the Hybrid-IR (<i>P</i> &lt; .001 for all); standard deviation (<i>P</i> = .002), energy (<i>P</i> &lt; .001), and entropy (<i>P</i> &lt; .001) of the DLIR-M; and standard deviation (<i>P</i> = .003), energy (<i>P</i> &lt; .001), entropy (<i>P</i> &lt; .001), and kurtosis (<i>P</i> = .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 (<i>P</i> = .20), while both outperformed DLIR-H (0.85) (<i>P</i> = .02–.04).</p> Conclusion <p>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.</p>

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Impact of image reconstruction algorithms on histogram analysis of iodine concentration for assessing chemotherapy response in pancreatic ductal adenocarcinoma

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Masashi Asano,
  • Yoshifumi Noda,
  • Nobuyuki Kawai,
  • Tetsuro Kaga,
  • Shingo Omata,
  • Yukiko Takai,
  • Akio Ito,
  • Takeshi Iwata,
  • Toshiharu Miyoshi,
  • Abdelazim Elsayed Elhelaly,
  • Hirohiko Imai,
  • Hiroki Kato,
  • Masayuki Matsuo

摘要

Purpose

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).

Methods

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.

Results

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).

Conclusion

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.