Breast cancer detection using enhanced IRI-numerical engine and inverse heat transfer modeling: model description and clinical validation.

Effective treatment of breast cancer relies heavily on early detection. Routine annual mammography is a widely accepted screening technique that has resulted in significantly improving the survival rate. However, it suffers from low sensitivity resulting in high false positives from screening. To overcome this problem, adjunctive technologies such as ultrasound are employed on about 10% of women recalled for additional screening following mammography. These adjunctive techniques still result in a significant number of women, about 1.6%, who undergo biopsy while only 0.4% of women screened have cancers. The main reason for missing cancers during mammography screening arises from the masking effect of dense breast tissue. The presence of a tumor results in the alteration of temperature field in the breast, which is not influenced by the tissue density. In the present paper, the IRI-Numerical Engine is presented as an adjunct for detecting cancer from the surface temperature data. It uses a computerized inverse heat transfer approach based on Pennes's bioheat transfer equations. Validation of this enhanced algorithm is conducted on twenty-three biopsy-proven breast cancer patients after obtaining informed consent under IRB protocol. The algorithm correctly predicted the size and location of cancerous tumors in twenty-four breasts, while twenty-two contralateral breasts were also correctly predicted to have no cancer (one woman had bilateral breast cancer). The tumors are seen as highly perfused and metabolically active heat sources that alter the surface temperatures that are used in heat transfer modeling. Furthermore, the results from this study with twenty-four biopsy-proven cancer cases indicate that the detection of breast cancer is not affected by breast density. This study indicates the potential of the IRI-Numerical Engine as an effective adjunct to mammography. A large scale clinical study in a statistically significant sample size is needed before integrating this approach in the current protocol.

Serum albumin is independently associated with higher mortality in adult sickle cell patients: Results of three independent cohorts.

Sickle cell disease (SCD) impacts liver and kidney function as well as skin integrity. These complications, as well as the hyperinflammatory state of SCD, could affect serum albumin. Serum albumin has key roles in antioxidant, anti-inflammatory and antithrombotic pathways and maintains vascular integrity. In SCD, these pathways modulate disease severity and clinical outcomes. We used three independent SCD adult cohorts to assess clinical predictors of serum albumin as well its association with mortality. In 2553 SCD adult participants, the frequency of low (<35 g/L) serum albumin was 5%. Older age and lower hemoglobin (P <0.001) were associated with lower serum albumin in all three cohorts. In age and hemoglobin adjusted analysis, higher liver enzymes (P <0.05) were associated with lower serum albumin. In two of the three cohorts, lower kidney function as measured by Glomerular Filtration Rate (P<0.001) was associated with lower serum albumin. Lower serum albumin predicted higher risk of tricuspid regurgitation velocity ≥ 2.5 m/s (OR = 1.1 per g/L, P ≤0.01). In all three cohorts, patients with low serum albumin had higher mortality (adjusted HR ≥2.9, P ≤0.003). This study confirms the role of serum albumin as a biomarker of disease severity and prognosis in patients with SCD. Albumin as a biomarker and possible mediator of SCD severity should be studied further.