Development and Validation of a Nomogram for Early Detection of Malignant Gallbladder Lesions Article Swipe
Mingyu Chen
,
Jiasheng Cao
,
Yang Bai
,
Chenhao Tong
,
Jian Lin
,
Vishal Jindal
,
Leandro Cardoso Barchi
,
Silvio Nadalin
,
Sherry X. Yang
,
Antonio Pesce
,
Fabrizio Panaro
,
Arié Ariche
,
Keita Kai
,
Riccardo Memeo
,
Tanios Bekaii‐Saab
,
Xiujun Cai
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.14309/ctg.0000000000000098
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.14309/ctg.0000000000000098
OBJECTIVES: Preoperative decision-making for differentiating malignant from benign lesions in the gallbladder remains challenging. We aimed to create a diagnostic nomogram to identify gallbladder cancer (GBC), especially for incidental GBC (IGBC), before surgical resection. METHODS: A total of 587 consecutive patients with pathologically confirmed gallbladder lesions from a hospital were randomly assigned to a training cohort (70%) and an internal validation cohort (30%), with 287 patients from other centers as an external validation cohort. Radiological features were developed by the least absolute shrinkage and selection operator logistic regression model. Significant radiological features and independent clinical factors, identified by multivariate analyses, were used to construct a nomogram. RESULTS: A diagnostic nomogram was established by age, CA19.9, and 6 radiological features. The values of area under the curve in the internal and external validation cohorts were up to 0.91 and 0.89, respectively. The calibration curves for probability of GBC showed optimal agreement between nomogram prediction and actual observation. Compared with previous methods, it demonstrated superior sensitivity (91.5%) and accuracy (85.1%) in the diagnosis of GBC. The accuracy using the nomogram was significantly higher in GBC groups compared with that by radiologists in the training cohort ( P < 0.001) and similarly in each cohort. Notably, most of the IGBC, which were misdiagnosed as benign lesions, were successfully identified using this nomogram. DISCUSSION: A novel nomogram provides a powerful tool for detecting the presence of cancer in gallbladder masses, with an increase in accuracy and sensitivity. It demonstrates an unprecedented potential for IGBC identification.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14309/ctg.0000000000000098
- OA Status
- gold
- Cited By
- 31
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2982530296
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2982530296Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14309/ctg.0000000000000098Digital Object Identifier
- Title
-
Development and Validation of a Nomogram for Early Detection of Malignant Gallbladder LesionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-01Full publication date if available
- Authors
-
Mingyu Chen, Jiasheng Cao, Yang Bai, Chenhao Tong, Jian Lin, Vishal Jindal, Leandro Cardoso Barchi, Silvio Nadalin, Sherry X. Yang, Antonio Pesce, Fabrizio Panaro, Arié Ariche, Keita Kai, Riccardo Memeo, Tanios Bekaii‐Saab, Xiujun CaiList of authors in order
- Landing page
-
https://doi.org/10.14309/ctg.0000000000000098Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.14309/ctg.0000000000000098Direct OA link when available
- Concepts
-
Nomogram, Medicine, Gallbladder cancer, Cohort, Radiology, Logistic regression, Radiological weapon, Gallbladder, Oncology, Surgery, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
31Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 3, 2023: 4, 2022: 9, 2021: 7Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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