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Research article
Xiuhong Shan
Affiliated People's Hospital of Jiangsu University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4257-6174
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background:
This study aimed to evaluate the value of radiomic nomogram in predicting lymph node metastasis in T1-2 gastric cancer according to the No. 3 station lymph nodes.
Methods:
A total of 159 T1-2 gastric cancer (GC) patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a primary cohort (n = 80) and a validation cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station lymph nodes (LN) based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve.
Results:
Two radiomic signatures, reflecting phenotypes of the tumor and LN respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the primary cohort (AUC: 0.915; 95% confidence interval [CI]: 0.832-0.998) and validation cohort (AUC: 0.908; 95%CI: 0.814-1.000). The decision curve also indicated its potential clinical usefulness.
Conclusions:
The nomogram received favorable predictive accuracy in predicting No.3 station LN metastasis in T1-2 GC, and could assist the choice of therapy.
Keywords
Stomach Cancer, Lymph Nodes, Nomogram
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CURRENT STATUS: Published
View the published article at BMC Medical Imaging.
Version 1
Posted 14 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 26 Jan, 2021
Review #1 received
Received 24 Jan, 2021
Review #2 received
Received 12 Jan, 2021
Reviewer #2 agreed
On 07 Jan, 2021
Editor assigned
On 06 Jan, 2021
Reviewers invited
Invitations sent on 06 Jan, 2021
Reviewer #1 agreed
On 06 Jan, 2021
Submission checks complete
On 06 Jan, 2021
Editor invited
On 06 Jan, 2021
First submitted
On 17 Dec, 2020
Metrics
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Subject Areas
Nuclear Medicine & Medical Imaging
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Medical Imaging.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Xiuhong Shan
Affiliated People's Hospital of Jiangsu University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4257-6174
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Imaging.
Version 1
Posted 14 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 26 Jan, 2021
Review #1 received
Received 24 Jan, 2021
Review #2 received
Received 12 Jan, 2021
Reviewer #2 agreed
On 07 Jan, 2021
Editor assigned
On 06 Jan, 2021
Reviewers invited
Invitations sent on 06 Jan, 2021
Reviewer #1 agreed
On 06 Jan, 2021
Submission checks complete
On 06 Jan, 2021
Editor invited
On 06 Jan, 2021
First submitted
On 17 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Nuclear Medicine & Medical Imaging
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Imaging.
Background:
This study aimed to evaluate the value of radiomic nomogram in predicting lymph node metastasis in T1-2 gastric cancer according to the No. 3 station lymph nodes.
Methods:
A total of 159 T1-2 gastric cancer (GC) patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a primary cohort (n = 80) and a validation cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station lymph nodes (LN) based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve.
Results:
Two radiomic signatures, reflecting phenotypes of the tumor and LN respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the primary cohort (AUC: 0.915; 95% confidence interval [CI]: 0.832-0.998) and validation cohort (AUC: 0.908; 95%CI: 0.814-1.000). The decision curve also indicated its potential clinical usefulness.
Conclusions:
The nomogram received favorable predictive accuracy in predicting No.3 station LN metastasis in T1-2 GC, and could assist the choice of therapy.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
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