We are conducting ongoing research to provide the most up-to-date data on the sensitivity, specificity, and overall accuracy of the MasSpec Pen on various cancer types.
The MasSpec Pen is currently for research only.
The MasSpec Pen has been tested on a number of different cancer types. Based on published research, the MasSpec Pen has achieved a high sensitivity (96.4%), specificity (96.2%), and overall accuracy (96.3%) on 253 unique tissue samples. Our research is continuing to grow as we expand our sample size and introduce new cancer types. Additionally, we are currently testing the MasSpec Pen for use in other applications where tissue deferentiation is important to clinical outcomes. You can find some of our research below.
Histologic discrimination between benign and malignant thyroid tissue is a clinical challenge, specially for follicular lesions. Surgery is often unnecessary in up to 60% of indeterminate cases as determined by fine needle aspirations biopsies. We achieved a sensitivity of 94.4%, specificity of 100.0% and an overall accuracy 97.8% when distinguishing between normal thyroid tissue (n=27) and papillary carcinoma (n=18). Similarly, our model predicted well between normal thyroid tissue (n=27) and follicular adenoma (n=11) with a sensitivity 90.9%, specificity 96.3% and overall accuracy of 94.7%.
Many women diagnosed with breast cancer undergo breast conserving surgery, which involves removing the lesion of interest with a rim of normal tissue and preserving the rest of the breast. Determining this delicate boundary between cancerous and normal tissues is important to achieve negative margins for invasive and carcinoma in situ while optimizing aesthetic outcomes. We are able to distinguish normal breast tissue (n=29) from ductal carcinoma (n=16) with a sensitivity 87.5%, specificity 100.0% and overall accuracy 95.6%.
Optimal surgical treatment of lung carcinomas includes complete local resection of the primary tumor because adverse patient outcome is strongly associated with residual tumor at the bronchial resection margin. We have achieved a sensitivity 88.2%, specificity 93.6% and overall accuracy of 92.2% when distinguishing between normal lung tissue (n=47) and adenocarcinoma (n=17). Our model performed similarly when distinguishing normal lung tissue (n=47) from squamous cell carcinoma (n=17) with a sensitivity 88.2%, specificity 95.7% and overall accuracy 93.8%.
For high-grade serous ovarian cancer (HGSC) patients, postoperative residual disease after surgical debulking is also negatively associated with progression-free survival and response to adjuvant chemotherapy. Thus, accurate negative margin assessment and complete tumor excision are highly desirable across cancer surgeries because they offer the greatest potential for prolonged disease-free and overall survival. We were able to determine normal ovarian tissue (n=28) from high-grade serous ovarian cancer (n=28) with a sensitivity 100.0%, specificity 89.7% and overall accuracy 94.7%
*Sensitivity is defined as the percentage of correctly identified cancerous samples. Specificity is defined as the percentage of correctly identified normal samples. Accuracy is defined as the average of sensitivity and specificity.