Evaluating Size of Lung Masses

Purpose

The increase, decrease, or stability of a known primary tumor is the starting point of all cancer staging exams. The goal would be to develop an algorithm that given a cancer study could determine the dominant lesion. Once that lesion is identified on the current study, It should be found on a comparison study and the volumes should be compared.

Tag(s)

 

Panel

Thoracic Panel

Define-AI ID

19080010

Originator

Christopher P. Gange
Lead Christopher P. Gange

Panel Chair

Eric Stern

Panel Reviewers

Thoracic Panel

License

Creative Commons 4.0 
Status Public Commenting 
RadElement Set RDES82
                               

Clinical Implementation


Value Proposition

Staging cancer accurately is a common and time consuming task in radiology, and lung cancer is the most common lethal malignancy in the world. An algorithm that automated these comparisons would improve radiologist’s workflow, and would hopefully decrease variability in reporting. This would allow oncologists to make better treatment decisions for cancer patients.

Narrative(s)

A patient being treated with a targeted therapy for lung cancer goes for their routine staging scan before a clinic visit. The algorithm flags this study as a significant increase in tumor volume, alerting the radiologist to read the study more promptly and look for signs of metastatic disease. The accurate staging allows the patient to get optimized oncology care.
A patient undergoing chemotherapy goes for their routine staging scan. The algorithm allows a quick read and the patient receives the good news of a treatment response before they leave the clinic.

Workflow Description

The image is obtained from modality and sent to PACS and the AI engine. The image is analyzed by the engine. The system then detects the primary tumor and reports its size. Size measurements are generated on all subsequent CT studies to determine how the tumor size is changing over time. An alert message is sent to PACS from the engine with the size information, identification, and graphic highlighting the tumor and its change.

Considerations for Dataset Development



Age

Varied

Sex at birth

Male, Female

Tumor size

Varied

Smoking history

Yes, No

Chronic obstructive pulmonary disease

absent, present

Diffuse lung fibrosis

absent, present

Tumor histology

adenocarcinoma, squamous cell, Large cell, small cell, carcinoid

Technical Specifications


Inputs

 

Current Chest CT

Procedure

Chest CT

Views

axial, sagittal, coronal

Data Type

DICOM

Modality

CT

Body Region

Chest

Anatomic Focus

Lung

 
 
Previous Chest CT

Procedure

Chest CT

Views

axial, sagittal, coronal

Data Type

DICOM

Modality

CT

Body Region

Chest

Anatomic Focus

Lung


Primary Outputs

Change in mass size

RadElement ID

RDE458

Definition

Signal a change in mass size

Data Type

Categorical

Value Set

  • Mass decreased in size

  • No change in mass size

  • Mass increased in size

Units

N/A

Current tumor volume

RadElement ID

RDE455

Definition

Lung tumor volume on chest CT

Data Type

Numeric

Value Set

N/A

Units

mm3


Tumor volume change

RadElement ID

RDE459

Definition

Signal a change in tumor volume

Data Type

Categorical

Value Set

  • Decrease in tumor volume

  • No change in tumor volume

  • Increase in tumor volume

Units

N/A

Tumor max diameter

RadElement ID

RDE456

Definition

Tumor max length (in mm) in any place

Data Type

Numeric

Value Set

N/A

Units

mm