ArticleQuantitative imaging in medicine and surgery2024
A prediction model based on dual-layer spectral detector computed tomography for distinguishing nonluminal from luminal invasive breast cancer.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The identification of the molecular subtypes of breast cancer is critical to determining appropriate treatment strategies and assessing prognosis. This study aimed to evaluate the ability of dual-layer spectral detector computed tomography (DLCT) metrics to differentiate luminal from nonluminal invasive breast cancer. Methods: A total of 220 patients with invasive breast cancer who underwent routine DLCT examination were included in the study. The molecular subtypes of breast cancer were identified through immunohistochemical staining of biopsies or postoperative pathological specimens. DLCT quantitative parameters were compared between the luminal and nonluminal types of breast cancer. The diagnostic efficacy of these parameters was determined via receiver operating characteristic (ROC) curves. Univariate and multivariate regression analyses were conducted to identify independent predictors that could differentiate nonluminal from luminal breast cancer. A nomogram prediction model was established based on multivariate regression analysis. The performance of the nomogram model was assessed with ROC curve and calibration curve analyses. Results: Among the DLCT quantitative values, eight were significantly lower in the luminal type than in the nonluminal type of breast cancer (P<0.001-0.011). The area under the curve (AUC) values for these significant DLCT quantitative parameters ranged from 0.604 to 0.694. Multivariate logistic regression analysis identified CT-reported lymph node metastasis status [hazard ratio (HR) =4.214; P<0.001], the Hounsfield unit (HU) value of the virtual monoenergetic image at 40 keV (HU Conclusions: DLCT quantitative parameters are valuable noninvasive indexes for differentiating between the luminal and nonluminal types of invasive breast cancer. Furthermore, the nomogram constructed in this study could guide individualized predictions of molecular subtypes in patients with invasive breast cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.