ArticleLa Radiologia medica2023
Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Review
- Radiomics models for predicting occult nodal metastasis in stage cT1a-bN0M0 lung adenocarcinoma: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- CT radiomics from intratumor and peritumor regions for predicting poorly differentiated invasive nonmucinous pulmonary adenocarcinoma.Scientific reports · 2025Article
- Impact of Tumor Size and Differentiation Grade on Survival After Lobectomy and Segmentectomy for Patients with Early-Stage Lung Adenocarcinoma.Annals of surgical oncology · 2024Article
- Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT.La Radiologia medica · 2024Article
- A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study.Frontiers in oncology · 2024Article
- Review
- The solid component within part-solid nodules: 3-dimensional quantification, correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas, and comparisons with 2-dimentional measures and semantic features in low-dose computed tomography.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
purposePoorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists. MATERIALS AND
methodsA total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity.
resultsNo significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05).
conclusionsThe radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.
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