ArticleJapanese journal of radiology2026
Deep learning model for the prediction of lymph node metastasis in esophageal squamous cell carcinoma using MIP FDG-PET images: a retrospective validation study.
Article in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAccurate preoperative diagnosis of lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC) is crucial for determining treatment strategies, including organ-sparing therapies. We aimed to develop and validate a deep learning (DL) model using rotational maximum-intensity projection (MIP) 18 F-FDG PET images to improve the prediction of LN metastasis. MATERIALS AND
methodsThis retrospective study included 185 patients with ESCC (146 receiving neoadjuvant chemotherapy) who underwent preoperative imaging using a silicon photomultiplier PET scanner. A convolutional neural network (CNN) was developed using six rotational MIP images (angles from - 60° to 90°). The architecture utilized a ResNet-50 backbone with weight sharing across views. The ground truth for LN metastasis was established via postoperative histopathology, including Grade 3 pathological response as positive. The performance of the model in the test set (n = 36) was compared with that of radiologist reports and SUVmax analysis using the area under the receiver operating characteristic curve (AUC) and diagnostic accuracy.
resultsThe CNN model achieved an AUC of 0.82 (95% CI 0.57-0.94), which was higher but not significantly different from the SUVmax method (0.77, p > 0.05). Although the differences were not statistically significant (p > 0.05), the CNN model achieved the highest absolute diagnostic accuracy of 86% at the optimal threshold, compared with the SUVmax-based method (67%), the clinical data model (75%), and radiologist reports (69%). Notably, while the CNN model exhibited a lower specificity (75%) than radiologist reports (92%), it demonstrated high sensitivity (92%) compared to radiologist (58%) and SUVmax (63%), effectively serving as a diagnostic safety net.
conclusionOur PET-based CNN model using rotational MIP images demonstrated comparable diagnostic accuracy and higher sensitivity for LN metastasis in ESCC than the conventional methods. This proof-of-concept highlights its potential as a preoperative tool for optimizing treatment selection and minimizing understaging.
Indexed as
Identifiers
42758372What 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.