Evidence map›Paper›PMID 42362600›Full record

ArticleScientific data2026

Advance Learning in Oncology - A CT Imaging Dataset of Lung Nodule Metastases from Bone and Soft Tissue Tumors.

Shaobo Han, Hao Wang, Wenjian Sun, Xiang Liu, Wacili Da, Yang Luo, Li Min, Chunbo Luo

Abstract readDataset
PubMed Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Shaobo HanSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Hao WangOrthopedics Research Institute, Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China.
Wenjian SunYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001, China.
Xiang LiuYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001, China.
Wacili DaOrthopedics Research Institute, Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China.
Yang LuoSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Li MinOrthopedics Research Institute, Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China. minli1204@scu.edu.cn.
Chunbo LuoSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China. c.luo@uestc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bone and soft tissue tumors (BSTTs) are prone to metastasize to the lungs to form pulmonary nodules, which are quite different from other metastatic nodules or primary nodules, such as those from lung, breast, or colorectal cancers. Early detection of these specific lung nodules is crucial for timely intervention and treatment of BSTTs. To promote the development of BSTTs detection algorithm based on deep learning, this paper releases a Computed Tomography (CT) dataset containing 59 patients and 779 BSTTs metastatic pulmonary nodules with pixel-level annotations. We further publish benchmark lung nodule detection experiments on this dataset, achieving F1 score of 0.842, respectively, demonstrating its research potential. We release raw CT data, preprocessing codes for data conversion and coordinate extraction, to enable researchers to obtain precisely calibrated datasets for training deep learning models and promote the utilization of this clinical scientific data.

Indexed as

Bone NeoplasmsDeep LearningLung NeoplasmsSoft Tissue NeoplasmsTomography, X-Ray ComputedHumans

Identifiers

PMID42362600

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.