Evidence map›Paper›PMID 40358704›Full record

ArticleAbdominal radiology (New York)2025

The utility of low-dose pre-operative CT of ovarian tumor with artificial intelligence iterative reconstruction for diagnosing peritoneal invasion, lymph node and hepatic metastasis.

Xiaojia Cai, Jintao Han, Wanhui Zhou, Fan Yang, Jing Liu, Qi Wang, Ruxun Li

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

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4citing papers in PubMed, 1 pooled it
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4 citing papers in PubMed, 1 synthesis or guideline pooled it.

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5 · Who and what money

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7 authors.

Xiaojia CaiDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Jintao HanUnited Imaging Healthcare, Shanghai, China.
Wanhui ZhouUnited Imaging Healthcare, Shanghai, China.
Fan YangDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Jing LiuDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Qi WangDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Ruxun LiDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China. liruxun123456688@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDiagnosis of peritoneal invasion, lymph node metastasis, and hepatic metastasis is crucial in the decision-making process of ovarian tumor treatment. This study aimed to test the feasibility of low-dose abdominopelvic CT with an artificial intelligence iterative reconstruction (AIIR) for diagnosing peritoneal invasion, lymph node metastasis, and hepatic metastasis in pre-operative imaging of ovarian tumor.

methodsThis study prospectively enrolled 88 patients with pathology-confirmed ovarian tumors, where routine-dose CT at portal venous phase (120 kVp/ref. 200 mAs) with hybrid iterative reconstruction (HIR) was followed by a low-dose scan (120 kVp/ref. 40 mAs) with AIIR. The performance of diagnosing peritoneal invasion and lymph node metastasis was assessed using receiver operating characteristic (ROC) analysis with pathological results serving as the reference. The hepatic parenchymal metastases were diagnosed and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured. The perihepatic structures were also scored on the clarity of porta hepatis, gallbladder fossa and intersegmental fissure.

resultsThe effective dose of low-dose CT was 79.8% lower than that of routine-dose scan (2.64 ± 0.46 vs. 13.04 ± 2.25 mSv, p < 0.001). The low-dose AIIR showed similar area under the ROC curve (AUC) with routine-dose HIR for diagnosing both peritoneal invasion (0.961 vs. 0.960, p = 0.734) and lymph node metastasis (0.711 vs. 0.715, p = 0.355). The 10 hepatic parenchymal metastases were all accurately diagnosed on the two image sets. The low-dose AIIR exhibited higher SNR and CNR for hepatic parenchymal metastases and superior clarity for perihepatic structures.

conclusionIn low-dose pre-operative CT of ovarian tumor, AIIR delivers similar diagnostic accuracy for peritoneal invasion, lymph node metastasis, and hepatic metastasis, as compared to routine-dose abdominopelvic CT. It is feasible and diagnostically safe to apply up to 80% dose reduction in CT imaging of ovarian tumor by using AIIR.

Indexed as

Artificial IntelligenceLiver NeoplasmsLymphatic MetastasisOvarian NeoplasmsPeritoneal NeoplasmsRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultAgedContrast MediaFeasibility StudiesFemaleHumansMiddle AgedNeoplasm InvasivenessPreoperative CareContrast MediaArtificial intelligenceComputed tomographyLow doseMetastasisOvarian tumor

Identifiers

PMID40358704

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.