Clinical Summary
World Journal of Surgical Oncology
September 2024

Independent performance cohort validates that DecisionDx-Melanoma provides personalized precision estimates for sentinel lymph node positivity

REFERENCE

Kriza, C., Martin, B., Bailey, C.N. et al. Integrating the melanoma 31-gene expression profile test with clinical and pathologic features can provide personalized precision estimates for sentinel lymph node positivity: an independent performance cohort. World J Surg Onc. 22, 228 (2024). [.underline-move-left]https://doi.org/10.1186/s12957-024-03512-4 [.underline-move-left]

View PublicationRequest a meeting
Introduction

Up to 88% of sentinel lymph node biopsies (SLNBs) are negative. The 31-gene expression profile (31-GEP) test can help identify patients with a low risk of SLN metastasis who can safely forego SLNB. The 31-GEP classifies patients as low (Class 1A), intermediate (Class 1B/2A), or high risk (Class 2B) for recurrence, metastasis, and SLN positivity. The integrated i31-SLNB combines the 31-GEP risk score with clinicopathologic features using a neural network algorithm to personalize SLN risk prediction.

Methods

Patients from a single surgical center with 31-GEP results were included (n=156). An i31-SLNB risk prediction <5% was considered low risk of SLN positivity. Chi-square was used to compare SLN positivity rates between groups.

Results

DecisionDx-Melanoma outperformed AJCCv8 in identifying patients who should undergo an SLNB

  • Patients with T1-T2 tumors and i31-SLNB positivity risk >10% had a SLN positivity rate of 33.3%, outperforming AJCCv8 in identifying patients who should undergo an SLNB.
  • Using the i31-SLNB to guide SLNB decisions could have significantly reduced the number of unnecessary SLNBs by 19.2% (30/156, p<0.001) for all patients, and 33% (30.91, p<0.001) for T1-T2 tumors.
  • Patients considered low risk by the i31-SLNB score had a 0% (0/30) SLN positivity rate compared to 31.9% (30/94, p<0.001) positivity rate in those with >10% risk.
Conclusion

The  i31-SLNB identified patients with <5% risk of SLN positivity who could safely forego SLNB. Combining the 31-GEP with clinicopathologic features for a precise risk estimate can help guide risk-aligned patient care decisions for SLNB to reduce the number of unnecessary SLNBs and increase the SLNB positivity yield if the procedure is performed.

Want to learn more about
DecisionDx-Melanoma?

Contact Us
Clinical Summary
World Journal of Surgical Oncology
September 2024

Independent performance cohort validates that DecisionDx-Melanoma provides personalized precision estimates for sentinel lymph node positivity

REFERENCE

Kriza, C., Martin, B., Bailey, C.N. et al. Integrating the melanoma 31-gene expression profile test with clinical and pathologic features can provide personalized precision estimates for sentinel lymph node positivity: an independent performance cohort. World J Surg Onc. 22, 228 (2024). [.underline-move-left]https://doi.org/10.1186/s12957-024-03512-4 [.underline-move-left]

View PublicationRequest a meeting
Introduction

Up to 88% of sentinel lymph node biopsies (SLNBs) are negative. The 31-gene expression profile (31-GEP) test can help identify patients with a low risk of SLN metastasis who can safely forego SLNB. The 31-GEP classifies patients as low (Class 1A), intermediate (Class 1B/2A), or high risk (Class 2B) for recurrence, metastasis, and SLN positivity. The integrated i31-SLNB combines the 31-GEP risk score with clinicopathologic features using a neural network algorithm to personalize SLN risk prediction.

Methods

Patients from a single surgical center with 31-GEP results were included (n=156). An i31-SLNB risk prediction <5% was considered low risk of SLN positivity. Chi-square was used to compare SLN positivity rates between groups.

Results

DecisionDx-Melanoma outperformed AJCCv8 in identifying patients who should undergo an SLNB

  • Patients with T1-T2 tumors and i31-SLNB positivity risk >10% had a SLN positivity rate of 33.3%, outperforming AJCCv8 in identifying patients who should undergo an SLNB.
  • Using the i31-SLNB to guide SLNB decisions could have significantly reduced the number of unnecessary SLNBs by 19.2% (30/156, p<0.001) for all patients, and 33% (30.91, p<0.001) for T1-T2 tumors.
  • Patients considered low risk by the i31-SLNB score had a 0% (0/30) SLN positivity rate compared to 31.9% (30/94, p<0.001) positivity rate in those with >10% risk.
Conclusion

The  i31-SLNB identified patients with <5% risk of SLN positivity who could safely forego SLNB. Combining the 31-GEP with clinicopathologic features for a precise risk estimate can help guide risk-aligned patient care decisions for SLNB to reduce the number of unnecessary SLNBs and increase the SLNB positivity yield if the procedure is performed.

Want to learn more about
DecisionDx-SCC?

Contact Us
Clinical Summary
World Journal of Surgical Oncology
September 2024

Independent performance cohort validates that DecisionDx-Melanoma provides personalized precision estimates for sentinel lymph node positivity

REFERENCE

Kriza, C., Martin, B., Bailey, C.N. et al. Integrating the melanoma 31-gene expression profile test with clinical and pathologic features can provide personalized precision estimates for sentinel lymph node positivity: an independent performance cohort. World J Surg Onc. 22, 228 (2024). [.underline-move-left]https://doi.org/10.1186/s12957-024-03512-4 [.underline-move-left]

View PublicationRequest a meeting
Introduction

Up to 88% of sentinel lymph node biopsies (SLNBs) are negative. The 31-gene expression profile (31-GEP) test can help identify patients with a low risk of SLN metastasis who can safely forego SLNB. The 31-GEP classifies patients as low (Class 1A), intermediate (Class 1B/2A), or high risk (Class 2B) for recurrence, metastasis, and SLN positivity. The integrated i31-SLNB combines the 31-GEP risk score with clinicopathologic features using a neural network algorithm to personalize SLN risk prediction.

Methods

Patients from a single surgical center with 31-GEP results were included (n=156). An i31-SLNB risk prediction <5% was considered low risk of SLN positivity. Chi-square was used to compare SLN positivity rates between groups.

Results

DecisionDx-Melanoma outperformed AJCCv8 in identifying patients who should undergo an SLNB

  • Patients with T1-T2 tumors and i31-SLNB positivity risk >10% had a SLN positivity rate of 33.3%, outperforming AJCCv8 in identifying patients who should undergo an SLNB.
  • Using the i31-SLNB to guide SLNB decisions could have significantly reduced the number of unnecessary SLNBs by 19.2% (30/156, p<0.001) for all patients, and 33% (30.91, p<0.001) for T1-T2 tumors.
  • Patients considered low risk by the i31-SLNB score had a 0% (0/30) SLN positivity rate compared to 31.9% (30/94, p<0.001) positivity rate in those with >10% risk.
Conclusion

The  i31-SLNB identified patients with <5% risk of SLN positivity who could safely forego SLNB. Combining the 31-GEP with clinicopathologic features for a precise risk estimate can help guide risk-aligned patient care decisions for SLNB to reduce the number of unnecessary SLNBs and increase the SLNB positivity yield if the procedure is performed.

Want to learn more about
Mypath-Melanoma?

Contact Us
Clinical Summary
World Journal of Surgical Oncology
September 2024

Independent performance cohort validates that DecisionDx-Melanoma provides personalized precision estimates for sentinel lymph node positivity

REFERENCE

Kriza, C., Martin, B., Bailey, C.N. et al. Integrating the melanoma 31-gene expression profile test with clinical and pathologic features can provide personalized precision estimates for sentinel lymph node positivity: an independent performance cohort. World J Surg Onc. 22, 228 (2024). [.underline-move-left]https://doi.org/10.1186/s12957-024-03512-4 [.underline-move-left]

View PublicationRequest a meeting
Introduction

Up to 88% of sentinel lymph node biopsies (SLNBs) are negative. The 31-gene expression profile (31-GEP) test can help identify patients with a low risk of SLN metastasis who can safely forego SLNB. The 31-GEP classifies patients as low (Class 1A), intermediate (Class 1B/2A), or high risk (Class 2B) for recurrence, metastasis, and SLN positivity. The integrated i31-SLNB combines the 31-GEP risk score with clinicopathologic features using a neural network algorithm to personalize SLN risk prediction.

Methods

Patients from a single surgical center with 31-GEP results were included (n=156). An i31-SLNB risk prediction <5% was considered low risk of SLN positivity. Chi-square was used to compare SLN positivity rates between groups.

Results

DecisionDx-Melanoma outperformed AJCCv8 in identifying patients who should undergo an SLNB

  • Patients with T1-T2 tumors and i31-SLNB positivity risk >10% had a SLN positivity rate of 33.3%, outperforming AJCCv8 in identifying patients who should undergo an SLNB.
  • Using the i31-SLNB to guide SLNB decisions could have significantly reduced the number of unnecessary SLNBs by 19.2% (30/156, p<0.001) for all patients, and 33% (30.91, p<0.001) for T1-T2 tumors.
  • Patients considered low risk by the i31-SLNB score had a 0% (0/30) SLN positivity rate compared to 31.9% (30/94, p<0.001) positivity rate in those with >10% risk.
Conclusion

The  i31-SLNB identified patients with <5% risk of SLN positivity who could safely forego SLNB. Combining the 31-GEP with clinicopathologic features for a precise risk estimate can help guide risk-aligned patient care decisions for SLNB to reduce the number of unnecessary SLNBs and increase the SLNB positivity yield if the procedure is performed.

Would you like to talk more about
data related to TissueCypher?

Contact Us