Glioma and other primary CNS neoplasms

Purpose	Estimation the histopathological type, grade, gene mutation and outcome of gliomas and other primary CNS neoplasms from MRI
Tag(s)
Panel	Neuroradiology
Define-AI ID	19020014
Originator	Houman Sotoudeh, MD
Lead	Houman Sotoudeh, MD
Panel Chair	Alex Norbash, MD
Panel Reviewers	Neuroradiology Panel
License	Creative Commons 4.0
Status	Public Commenting
RadElement Set	RDES115

                               

Clinical Implementation

Value Proposition

Primary CNS tumors are challenging conditions with glioma being the most common primary CNS neoplasm. The definite diagnosis of these primary CNS malignancies necessitates biopsy, pathological evaluation and gene sequencing. At this time accurate prediction of histopathology type, grading and especially gene mutation-outcome is not possible via evaluation of MR images. The role of “radiomics” is evolving and AI models are promising in this field. If an AI algorithm can be trained on the brain MRIs to predict the histopathology, grade, gene mutation and prognosis of glioma, the neurosurgeon and patients can be prepared for different treatment plans.

Narrative(s)

An otherwise healthy 30 year old man presents with recent onset seizure. The brain MRI shows an ill-defined T2/FLAIR hyper-signal intensity in anterior right temporal lobe with extension to the right insula without abnormal enhancement or increased rCBV on MR perfusion. Images are most consistent with low grade glioma but for the definite diagnosis the patient needs biopsy.

Workflow Description

Collecting the pre-op/pre-biopsy brain MRI in different sequences in patients with glioma and other primary CNS neoplasms. Collecting the pathological diagnosis, grade and gene mutation in each patient as well as survival rate, presence or absence pseudo-progression, pseudo-response, recurrence and radiation necrosis after surgery, chemo and radiation.

The image is obtained from MR scanner and sent to PACS and the AI engine. Images are analyzed by the engine. The system then detects and predicts the possible histopathology, grade, gene mutation and prognosis of glioma and other primary CNS neoplasms as well as a prediction about chance of developing radiation necrosis, pseudoprogression and psudo-response after treatments and finally estimated survival rate . An alert message is sent to PACS from the engine with the information and graphic highlighting these details.

Considerations for Dataset Development

The algorithm must be trained on all pre-op MRI sequences from patients with primary CNS neoplasm who are candidates for biopsy or surgical resection. The algorithm also must be trained with attention to the result of post biopsy/surgical histopathology report including the tumor type, grade, gene mutation as well as medical conditions after treatment including presence or absence of recurrence, radiation necrosis, pseudoresponse, pseudoprogression and finally the survival rate.

Technical Specifications

Inputs

DICOM Study

Primary Outputs

Tumor Classification

Tumor Grade

Identify mutated genes associated with gliomas

Secondary Outputs

Survival Probability

Pseudo-progression probability

Pseudo-response probability

Recurrence probability

Radiation necrosis probability

Future Development Ideas

Although the main target of the use case is glioma as the most common CNS primary tumor, since the database contains all primary CNS tumors the same prediction can be achieved for other non-glioma tumors (of course if we can collect enough cases).

In addition, in the future, additional gene mutations can be discovered in these tumors. Retraining of the algorithm for future mutations can be possible.

This dataset also can be used to facilitate the clinical trials for treating CNS tumors. Most of the trials work with inclusion criteria and they try to include the most homogeneous patient population into the trial. The same dataset can help the researchers to select more homogeneous patients for their trials.

Related Datasets

The Cancer Imaging Archive (TCIA)

ICDC-Glioma (GLIOMA01)	ACRIN-FMISO-Brain (ACRIN 6684)
QIN-HeadNeck	QIN-BRAIN-DSC-MRI
ACRIN-DSC-MR-Brain (ACRIN 6677/RTOG 0625)	TCGA-HNSC
AAPM RT-MAC Grand Challenge 2019	MRI-DIR
CPTAC-HNSCC	LGG-1p19qDeletion
HNSCC	IvyGAP
QIN GBM Treatment Response	TCGA-LGG
CPTAC-GBM	TCGA-GBM
ACRIN-HNSCC-FDG-PET/CT (ACRIN 6685)
OPC-Radiomics

 

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