CT Density in the Pancreas is a Promising Imaging Predictor for Pancreatic Ductal Adenocarcinoma

Yasunari Fukuda, Daisaku Yamada, Hidetoshi Eguchi, Tomoki Hata, Yoshifumi Iwagami, Takehiro Noda, Tadafumi Asaoka, Koichi Kawamoto, Kunihito Gotoh, Shogo Kobayashi, Yutaka Takeda, Masahiro Tanemura, Masaki Mori, Yuichiro Doki

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Abstract

Background: Fatty pancreas (FP) was recently recognized as a risk factor for pancreatic ductal adenocarcinoma (PDAC). It is unclear whether computed tomography (CT) can be used to make a FP diagnosis. This study investigated whether CT could provide a predictive value for PDAC by diagnosing FP. Methods: The study included 183 consecutive patients who underwent distal pancreatectomy from February 2007 to January 2017, including 75 cases of PDAC and 108 cases of other pancreatic disease. Pancreatic CT density (pancreatic index; PI) at the initial diagnosis was calculated by dividing the CT number in the pancreas by the number in the spleen. To assess whether CT could be used to detect FP, 43 cases were evaluated pathologically for FP. We investigated the correlation between FP and PI, and determined the optimal PI cutoff value for detecting FP using receiver operating characteristics analysis. We then investigated whether the PI value could be used as a predictor for PDAC. Results: Fourteen cases (32.6%) were pathologically diagnosed with FP. PI was significantly lower in the FP group versus the non-FP group (0.51 vs. 0.83; p = 0.0049). ROC analysis indicated that the PI had good diagnostic accuracy for FP diagnosis (cutoff value 0.70; sensitivity 0.79, specificity 0.79). Low PI (≤0.70) was identified in the multivariate analysis as an independent risk factor for PDAC (odds ratio 2.31; p = 0.023). Conclusions: PI was strongly associated with pathological FP, which was independently associated with PDAC. PI shows promise as an imaging predictor for PDAC.

Original languageEnglish
Pages (from-to)2762-2769
Number of pages8
JournalAnnals of Surgical Oncology
Volume24
Issue number9
DOIs
Publication statusPublished - Sep 1 2017

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Pancreas
Adenocarcinoma
Tomography
ROC Curve
Pancreatic Diseases
Pancreatectomy
Spleen
Multivariate Analysis
Odds Ratio
Sensitivity and Specificity

All Science Journal Classification (ASJC) codes

  • Surgery
  • Oncology

Cite this

Fukuda, Y., Yamada, D., Eguchi, H., Hata, T., Iwagami, Y., Noda, T., ... Doki, Y. (2017). CT Density in the Pancreas is a Promising Imaging Predictor for Pancreatic Ductal Adenocarcinoma. Annals of Surgical Oncology, 24(9), 2762-2769. https://doi.org/10.1245/s10434-017-5914-3

CT Density in the Pancreas is a Promising Imaging Predictor for Pancreatic Ductal Adenocarcinoma. / Fukuda, Yasunari; Yamada, Daisaku; Eguchi, Hidetoshi; Hata, Tomoki; Iwagami, Yoshifumi; Noda, Takehiro; Asaoka, Tadafumi; Kawamoto, Koichi; Gotoh, Kunihito; Kobayashi, Shogo; Takeda, Yutaka; Tanemura, Masahiro; Mori, Masaki; Doki, Yuichiro.

In: Annals of Surgical Oncology, Vol. 24, No. 9, 01.09.2017, p. 2762-2769.

Research output: Contribution to journalArticle

Fukuda, Y, Yamada, D, Eguchi, H, Hata, T, Iwagami, Y, Noda, T, Asaoka, T, Kawamoto, K, Gotoh, K, Kobayashi, S, Takeda, Y, Tanemura, M, Mori, M & Doki, Y 2017, 'CT Density in the Pancreas is a Promising Imaging Predictor for Pancreatic Ductal Adenocarcinoma', Annals of Surgical Oncology, vol. 24, no. 9, pp. 2762-2769. https://doi.org/10.1245/s10434-017-5914-3
Fukuda, Yasunari ; Yamada, Daisaku ; Eguchi, Hidetoshi ; Hata, Tomoki ; Iwagami, Yoshifumi ; Noda, Takehiro ; Asaoka, Tadafumi ; Kawamoto, Koichi ; Gotoh, Kunihito ; Kobayashi, Shogo ; Takeda, Yutaka ; Tanemura, Masahiro ; Mori, Masaki ; Doki, Yuichiro. / CT Density in the Pancreas is a Promising Imaging Predictor for Pancreatic Ductal Adenocarcinoma. In: Annals of Surgical Oncology. 2017 ; Vol. 24, No. 9. pp. 2762-2769.
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abstract = "Background: Fatty pancreas (FP) was recently recognized as a risk factor for pancreatic ductal adenocarcinoma (PDAC). It is unclear whether computed tomography (CT) can be used to make a FP diagnosis. This study investigated whether CT could provide a predictive value for PDAC by diagnosing FP. Methods: The study included 183 consecutive patients who underwent distal pancreatectomy from February 2007 to January 2017, including 75 cases of PDAC and 108 cases of other pancreatic disease. Pancreatic CT density (pancreatic index; PI) at the initial diagnosis was calculated by dividing the CT number in the pancreas by the number in the spleen. To assess whether CT could be used to detect FP, 43 cases were evaluated pathologically for FP. We investigated the correlation between FP and PI, and determined the optimal PI cutoff value for detecting FP using receiver operating characteristics analysis. We then investigated whether the PI value could be used as a predictor for PDAC. Results: Fourteen cases (32.6{\%}) were pathologically diagnosed with FP. PI was significantly lower in the FP group versus the non-FP group (0.51 vs. 0.83; p = 0.0049). ROC analysis indicated that the PI had good diagnostic accuracy for FP diagnosis (cutoff value 0.70; sensitivity 0.79, specificity 0.79). Low PI (≤0.70) was identified in the multivariate analysis as an independent risk factor for PDAC (odds ratio 2.31; p = 0.023). Conclusions: PI was strongly associated with pathological FP, which was independently associated with PDAC. PI shows promise as an imaging predictor for PDAC.",
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T1 - CT Density in the Pancreas is a Promising Imaging Predictor for Pancreatic Ductal Adenocarcinoma

AU - Fukuda, Yasunari

AU - Yamada, Daisaku

AU - Eguchi, Hidetoshi

AU - Hata, Tomoki

AU - Iwagami, Yoshifumi

AU - Noda, Takehiro

AU - Asaoka, Tadafumi

AU - Kawamoto, Koichi

AU - Gotoh, Kunihito

AU - Kobayashi, Shogo

AU - Takeda, Yutaka

AU - Tanemura, Masahiro

AU - Mori, Masaki

AU - Doki, Yuichiro

PY - 2017/9/1

Y1 - 2017/9/1

N2 - Background: Fatty pancreas (FP) was recently recognized as a risk factor for pancreatic ductal adenocarcinoma (PDAC). It is unclear whether computed tomography (CT) can be used to make a FP diagnosis. This study investigated whether CT could provide a predictive value for PDAC by diagnosing FP. Methods: The study included 183 consecutive patients who underwent distal pancreatectomy from February 2007 to January 2017, including 75 cases of PDAC and 108 cases of other pancreatic disease. Pancreatic CT density (pancreatic index; PI) at the initial diagnosis was calculated by dividing the CT number in the pancreas by the number in the spleen. To assess whether CT could be used to detect FP, 43 cases were evaluated pathologically for FP. We investigated the correlation between FP and PI, and determined the optimal PI cutoff value for detecting FP using receiver operating characteristics analysis. We then investigated whether the PI value could be used as a predictor for PDAC. Results: Fourteen cases (32.6%) were pathologically diagnosed with FP. PI was significantly lower in the FP group versus the non-FP group (0.51 vs. 0.83; p = 0.0049). ROC analysis indicated that the PI had good diagnostic accuracy for FP diagnosis (cutoff value 0.70; sensitivity 0.79, specificity 0.79). Low PI (≤0.70) was identified in the multivariate analysis as an independent risk factor for PDAC (odds ratio 2.31; p = 0.023). Conclusions: PI was strongly associated with pathological FP, which was independently associated with PDAC. PI shows promise as an imaging predictor for PDAC.

AB - Background: Fatty pancreas (FP) was recently recognized as a risk factor for pancreatic ductal adenocarcinoma (PDAC). It is unclear whether computed tomography (CT) can be used to make a FP diagnosis. This study investigated whether CT could provide a predictive value for PDAC by diagnosing FP. Methods: The study included 183 consecutive patients who underwent distal pancreatectomy from February 2007 to January 2017, including 75 cases of PDAC and 108 cases of other pancreatic disease. Pancreatic CT density (pancreatic index; PI) at the initial diagnosis was calculated by dividing the CT number in the pancreas by the number in the spleen. To assess whether CT could be used to detect FP, 43 cases were evaluated pathologically for FP. We investigated the correlation between FP and PI, and determined the optimal PI cutoff value for detecting FP using receiver operating characteristics analysis. We then investigated whether the PI value could be used as a predictor for PDAC. Results: Fourteen cases (32.6%) were pathologically diagnosed with FP. PI was significantly lower in the FP group versus the non-FP group (0.51 vs. 0.83; p = 0.0049). ROC analysis indicated that the PI had good diagnostic accuracy for FP diagnosis (cutoff value 0.70; sensitivity 0.79, specificity 0.79). Low PI (≤0.70) was identified in the multivariate analysis as an independent risk factor for PDAC (odds ratio 2.31; p = 0.023). Conclusions: PI was strongly associated with pathological FP, which was independently associated with PDAC. PI shows promise as an imaging predictor for PDAC.

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