NDLI: Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections

Content Provider	Springer Nature : BioMed Central
Author	Huang, Yuling Luo, Mingming Luo, Zan Liu, Mingzhi Li, Junyu Jian, Junming Zhang, Yun
Abstract	Background Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of four-dimensional (4D) computed tomography (CT), which leads to indirect results. This study proposed a DL-based method for automatic IGTV delineation using maximum and average intensity projections (MIP and AIP, respectively) from 4D CT. Methods We retrospectively enrolled 124 patients with NSCLC and divided them into training (70%, n = 87) and validation (30%, n = 37) cohorts. Four-dimensional CT images were acquired, and the corresponding MIP and AIP images were generated. The IGTVs were contoured on 4D CT and used as the ground truth (GT). The MIP or AIP images, along with the corresponding IGTVs (IGTVMIP−manu and IGTVAIP−manu, respectively), were fed into the DL models for training and validation. We assessed the performance of three segmentation models—U-net, attention U-net, and V-net—using the Dice similarity coefficient (DSC) and the 95th percentile of the Hausdorff distance (HD95) as the primary metrics. Results The attention U-net model trained on AIP images presented a mean DSC of 0.871 ± 0.048 and mean HD95 of 2.958 ± 2.266 mm, whereas the model trained on MIP images achieved a mean DSC of 0.852 ± 0.053 and mean HD95 of 3.209 ± 2.136 mm. Among the models, attention U-net and U-net achieved similar results, considerably surpassing V-net. Conclusions DL models can automate IGTV delineation using MIP and AIP images, streamline contouring, and enhance the accuracy and consistency of lung cancer radiotherapy planning to improve patient outcomes.
Related Links	https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-025-02642-7.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s13014-025-02642-7
Journal	Radiation Oncology
Issue Number	1
Volume Number	20
Language	English
Publisher	BioMed Central
Publisher Date	2025-04-18
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Radiotherapy Imaging Radiology Internal gross tumor volume Automatic delineation Deep learning Non-small cell lung cancer Four-dimensional computed tomography
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Determination of patient-specific internal gross tumor volumes for lung cancer using four-dimensional computed tomography

Reconstitution of internal target volumes by combining four-dimensional computed tomography and a modified slow computed tomography scan in stereotactic body radiotherapy planning for lung cancer

The clinical application of 4D 18F-FDG PET/CT on gross tumor volume delineation for radiotherapy planning in esophageal squamous cell cancer

Impact of target volume segmentation accuracy and variability on treatment planning for 4D-CT-based non-small cell lung cancer radiotherapy.

Application of four-dimensional cone beam computed tomography in lung cancer radiotherapy

FDG-PET/CT imaging for staging and target volume delineation in conformal radiotherapy of anal carcinoma

Changes in volume of stage I non-small-cell lung cancer during stereotactic body radiotherapy

Does Motion Assessment With 4-Dimensional Computed Tomographic Imaging for Non–Small Cell Lung Cancer Radiotherapy Improve Target Volume Coverage?

A Novel Deep Learning Framework for Internal Gross Target Volume Definition From 4D Computed Tomography of Lung Cancer Patients

Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections

Similar Documents

Determination of patient-specific internal gross tumor volumes for lung cancer using four-dimensional computed tomography

Reconstitution of internal target volumes by combining four-dimensional computed tomography and a modified slow computed tomography scan in stereotactic body radiotherapy planning for lung cancer

The clinical application of 4D 18F-FDG PET/CT on gross tumor volume delineation for radiotherapy planning in esophageal squamous cell cancer

Impact of target volume segmentation accuracy and variability on treatment planning for 4D-CT-based non-small cell lung cancer radiotherapy.

Application of four-dimensional cone beam computed tomography in lung cancer radiotherapy

FDG-PET/CT imaging for staging and target volume delineation in conformal radiotherapy of anal carcinoma

Changes in volume of stage I non-small-cell lung cancer during stereotactic body radiotherapy

Does Motion Assessment With 4-Dimensional Computed Tomographic Imaging for Non–Small Cell Lung Cancer Radiotherapy Improve Target Volume Coverage?

A Novel Deep Learning Framework for Internal Gross Target Volume Definition From 4D Computed Tomography of Lung Cancer Patients

Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections