ArticleTranslational cancer research2025
Spectral computed tomography iodine concentration spatial distribution and multi-parameter quantitative analysis in the differential diagnosis of central small cell lung cancer from squamous cell carcinoma.
Article in Translational cancer research, 2025. 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.
- Spectral CT quantitative parameters for predicting complete ablation after radiofrequency ablation in liver tumors: a retrospective study.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Central small cell lung cancer (SCLC) and squamous cell carcinoma (SCC) are distinct types of lung cancer that require different treatment approaches and have varying prognoses. Accurate differentiation is crucial for clinical decision-making. However, traditional computed tomography (CT) may face challenges when their imaging features overlap. Spectral CT provides additional spectral images, offering more objective information for differentiation. This study investigated the application values of iodine concentration spatial distribution and multi-parameter quantitative analysis using a novel double-layer detector energy spectrum CT in the differential diagnosis of SCLC and SCC. Methods: Sixty-six patients with central lung cancer (including 31 patients with SCLC and 35 patients with SCC) were examined with CT spectral imaging during arterial phase (AP) and venous phase (VP). The mean iodine concentration (IC), mean effective atomic number (Eff-Z), normalized iodine concentration (NIC), normalized iodine concentration in the proximal region (NICpro), normalized iodine concentration in the distal region (NICdis), and the differences in NIC between the proximal and distal regions in the AP and VP were calculated. The differences in parameters between central SCLC and SCC were compared, and receiver operating characteristic (ROC) curves were generated to explore the value of these parameters in distinguishing between central SCLC and SCC. Results: The Eff-Z, IC, and NIC of SCLC were higher than SCC in the AP and VP. In terms of the spatial distribution differences in iodine concentration, both NICpro and NICdis during AP and VP were higher in SCLC compared to SCC. Moreover, ROC analysis showed that the NICdis in the VP had highest diagnostic performance in differentiating SCLC from SCC with an area under the receiver operating characteristic curve of 0.897, sensitivity of 96.80%, and a critical threshold of 0.175. Conclusions: CT spectral imaging proves beneficial in diagnosing and differentiating between central SCLC and SCC, demonstrating significant clinical application value. Particularly, the spatial distribution of iodine concentration emerges as a valuable factor capable of enhancing diagnostic efficiency, showcasing undeniable potential.
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.