Extranodal Extension

Purpose

Detect/delineate lymph node involvement and extranodal extension on cross-sectional images

Tag(s)

 

Panel

Oncology 

TOUCH-AI ID

TAI-07180001 

Originator

Oncology Panel 

Panel Chair

Reid F. Thompson 

Panel Reviewers

Oncology Panel 

License

Creative Commons 4.0 

Status Public Commenting 
                               

Clinical Implementation


Value Proposition

Canonically, ECE is determined after time of surgical excision, often connoting a substantially worse prognosis at that time. This use case would be of most relevance for diagnoses where surgery occurs after a period of neoadjuvant therapy, and could enable treatment intensification prior to the finding of ECE at time of surgery. Moreover, a performant algorithm could potentially identify ECE for diagnoses that do not usually proceed to surgery, potentially enabling better treatment stratification in this population. Automated ECE classification and identification could also enable improved radiotherapy targeting of nodal basins, as well as treatment optimization for post-operative imaging-detected nodal disease. Specific examples of these scenarios include but are not limited to:

  • Head and neck cancers
  • Prostate cancer
  • Anal and colorectal cancers
  • Cervical and endometrial cancers 

Although not proven, this algorithm or a semi-automated approach could improve cancer outcomes and decrease morbidity.

Narrative(s)

60 year old male smoker undergoes CT of the head and neck for his newly diagnosed cancer of the oral cavity. Algorithm evaluates image and identifies all visible lymph nodes, classifies each of them as radiographically normal, involved by cancer, or indeterminate, and further identifies presence and location of any radiographically visible extranodal extension. If a radiologist is not present at the time of imaging, an alert is provided to the ordering physician. Algorithm results will be accessible as a DICOM-RT structure set.

Workflow Description

Image obtained from modality and sent to PACS and the AI engine. Image analyzed by engine. System detects and defines lymph node(s) and assigns probability of malignancy as well as presence of extranodal extension. An alert message is sent to PACS from the engine with the information, identification, and graphic highlighting segmented and labeled normal and abnormal lymph nodes as a DICOM-RT structure set object.

Considerations for Dataset Development


Technical Variance

Modality: {CT (helical, cone beam)}

 

Contrast: {agent, dose, route, protocol}

 

Scanner: {manufacturer, age, model, tabletop}

 

Setup devices: {aquaplast mask, breast board, etc.}

 

Positioning: {neck flexed/extended, arms down/up, legs frog-legged, etc.}

 

Artifacts: {dental or orthopedic hardware, patient motion, pixel loss}

 

Acquisition protocol: {scanning parameters (e.g. slice thickness), pulse sequence, etc.} 

 

Clinical Variance

Anatomical site: {ensure dataset includes supraclavicular, axillary, iliac, inguinal, and other areas of lymphadenopathy in addition to cervical and retropharyngeal lymphadenopathy}

 

Tumor type: {SCC, adenocarcinoma, salivary gland histologies, melanoma, other}

 

Viral status: {HPV subtypes, EBV, HIV}

 

Lymph node size: {numerous examples of sub-centimeter disease extending all the way to bulky lymphadenopathy}

 

Habitus: {height/weight/BMI, algorithm should be agnostic to cachexia, obesity, etc.}

 

Age: {algorithm should account for cases in juvenile/pediatric as well as very elderly contexts}

 

Competing diagnoses: {acute infection (e.g. viral), chronic infection (e.g. TB), autoimmune (e.g. SLE), lymphoma}

 

Confounders: {prior XRT, prior surgery or SLNB, prior trauma, birth defects}

 

Demographics: {sex, ethnicity}

Technical Specifications


Inputs

DICOM Study

Procedure

CT

Data Type

DICOM

Modality

CT

Anatomic Focus

Any

Scenario

Cancer Diagnosis

 

Primary Outputs

Lymph Node Identification

RadElement ID

RDE207

Definition

Detect and delineate visible lymph nodes

Data Type

DICOM-RT structure set

Value Set

3D structure coordinates

Units

N/A

Multiplicity

1 (single structure set returned with all detected lymph nodes)

 

Lymph Node Classification

RadElement ID

RDE208

Definition

Classify individual lymph nodes as radiographically normal, involved by cancer, or indeterminate

Data Type

Categorical

Value Set

0-Unknown

1-radiographically normal

2-involved by cancer

Units

N/A

Multiplicity

[0,] (repeated for each detected lymph node)

Future Development Ideas


Related Applications

  • Segment and describe lymph nodes with one or more of the RadElement descriptors (RDES24
  • Integration into cancer staging paradigms and workflows
  • Monitoring for disease recurrence
  • Computer-assisted or fully automated radiation treatment planning  

Challenges

  • Segmentation may provide significant difficulty depending on factors such as fibrosis, any post treatment changes, fat content, anatomical location, and image artifacts, among others.
  • Availability of pathologic confirmation of disease and localization of ECE may be particularly challenging at the dataset construction level

Public Commenting


Use cases are meant to be a primary vehicle for distributing clinical information to the developer community. They pinpoint precise scenarios within radiology workflows where potential automation could add noticeable value and establish standards for interpreting and passing corresponding common data elements. Implementing effective standards requires the perspective from all stakeholders. So to that end, we encourage your feedback on use cases.

To submit comments, please email DSIUseCases@acr.org with the use case title(s) and relevant comments by January 1, 2019. If more convenient, you may also download this use case and comment directly on the PDF. Just attach the PDF copy on the email.