In 1 to 2 percent of cancer cases, the primary site of tumor origin cannot be determined. Because many modern cancer therapeutics target primary tumors, the prognosis for a cancer of unknown primary (CUP) is poor, with a median overall survival of 2.7-to-16 months.
In order to receive a more specific diagnosis, patients often must undergo extensive diagnostic workups that can include additional laboratory tests, biopsies and endoscopy procedures, which delay treatment. To improve diagnosis for patients with complex metastatic cancers, especially those in low-resource settings, researchers developed an artificial intelligence (AI) system that uses routinely acquired histology slides to accurately find the origins of metastatic tumors while generating a "differential diagnosis," for CUP patients. Research findings are described in Nature.
"Almost every patient that has a cancer diagnosis has a histology slide, which has been the diagnostic standard for over a hundred years. Our work provides a way to leverage universally acquired data and the power of artificial intelligence to improve diagnosis for these complicated cases that typically require extensive diagnostic work-ups," said corresponding author.
The deep-learning-based algorithm developed by the researchers, called Tumor Origin Assessment via Deep Learning (TOAD), simultaneously identifies the tumor as primary or metastatic and predicts its site of origin.
The researchers trained their model with gigapixel pathology whole-slide images of tumors from over 22,000 cancer cases, and then tested TOAD in about 6,500 cases with known primaries and analyzed increasingly complicated metastatic cancers to establish utility of the AI model on CUPs. For tumors with known primary origins, the model correctly identified the cancer 83 percent of the time and listed the diagnosis among its top three predictions 96 percent of the time. The researchers then tested the model on 317 CUP cases for which a differential diagnosis was assigned, finding that TOAD's diagnosis agreed with pathologists' reports 61 percent of the time and top-three agreement in 82 percent of cases.
TOAD's performance was largely comparable to the performance reported by several recent studies that used genomic data to predict tumor origins. While genomic-based AI offers an alternative option for aiding diagnoses, genomic testing is not always performed for patients, especially in low-resource settings. The researchers hope to continue training their histology-based model with more cases and engage in clinical trials to determine whether it improves diagnostic capabilities and patients' prognoses.
"The top predictions from the model can accelerate diagnosis and subsequent treatment by reducing the number of ancillary tests that need to be ordered, reducing additional tissue sampling, and the overall time required to diagnose patients, which can be long and stressful," the author said. "Top-three predictions can be used to guide pathologists next steps, and in low-resource settings where pathology expertise may not be available the top prediction could potentially be used to assign a differential diagnosis. This is only the first step in using whole-slide images for AI-assisted cancer origin prediction, and it's a very exciting area with the potential to standardize and improve the diagnostic process."
https://www.nature.com/articles/s41586-021-03512-4
AI-based pathology predicts origins for cancers of unknown primary
- 1,479 views
- Added
Edited
Latest News
Brain hormone regulate both…
By newseditor
Posted 17 Mar
Blocking long non-coding RN…
By newseditor
Posted 17 Mar
Artificial intelligence and…
By newseditor
Posted 17 Mar
Blood-brain barrier protein…
By newseditor
Posted 17 Mar
Preventing heart attacks an…
By newseditor
Posted 17 Mar
Other Top Stories
Anti-seizure effect of ketogenic diet linked to gut microbiome
Read more
Microbiome differences between urban and rural populations start so…
Read more
Bacteria-based constipation treatment?
Read more
Natural products in bacteria unearthed
Read more
Long-term estrogen therapy changes microbial activity in the gut
Read more
Protocols
Integration of Kupffer cell…
By newseditor
Posted 18 Mar
A mouse DRG genetic toolkit…
By newseditor
Posted 17 Mar
An optogenetic method for t…
By newseditor
Posted 13 Mar
Profiling native pulmonary…
By newseditor
Posted 08 Mar
Neuromuscular organoids mod…
By newseditor
Posted 06 Mar
Publications
Synaptopathy: presynaptic c…
By newseditor
Posted 18 Mar
Allergic Rhinitis
By newseditor
Posted 18 Mar
ALK upregulates POSTN and W…
By newseditor
Posted 18 Mar
PRODH safeguards human naiv…
By newseditor
Posted 18 Mar
Secretin-dependent signals…
By newseditor
Posted 17 Mar
Presentations
Hydrogels in Drug Delivery
By newseditor
Posted 12 Apr
Lipids
By newseditor
Posted 31 Dec
Cell biology of carbohydrat…
By newseditor
Posted 29 Nov
RNA interference (RNAi)
By newseditor
Posted 23 Oct
RNA structure and functions
By newseditor
Posted 19 Oct
Posters
A chemical biology/modular…
By newseditor
Posted 22 Aug
Single-molecule covalent ma…
By newseditor
Posted 04 Jul
ASCO-2020-HEALTH SERVICES R…
By newseditor
Posted 23 Mar
ASCO-2020-HEAD AND NECK CANCER
By newseditor
Posted 23 Mar
ASCO-2020-GENITOURINARY CAN…
By newseditor
Posted 23 Mar