Spanish researchers testing a dedicated breast PET scanner found that it had a high false-negative rate for detecting in situ carcinomas in women with BI-RADS 4 lesions. The finding raises questions about the suitability of the system for working up suspicious breast lesions, according to a 3 October study in the European Journal of Radiology.
In a group of 50 patients with mammograms classified as BI-RADS 4, dedicated breast PET missed nine of 18 cancers. In addition, dedicated breast PET recorded increased metabolic activity in 10 benign lesions.
"Our analysis does not allow the recommendation of dedicated breast PET for diagnosis of malignancy in BI-RADS 4 mammographic or ultrasound abnormalities, given the high rate of false-negative results regarding in situ neoplasms," wrote lead author Dr. Lucía Graña-López from Hospital Lucus Augusti Lugo and colleagues.
Molecular breast imaging has come to the forefront in recent years as a tool that could help with the detection of breast cancer, especially among patients classified as BI-RADS 4 or greater. While mammography and ultrasound are generally the modalities of choice for workup, they also can produce a high number of false positives, as does breast MRI.
Could molecular imaging offer an alternative? The group from Hospital Lucus Augusti Lugo decided to investigate the potential of a dedicated PET scanner, (Mammi, OncoVision), that is available in Europe.
In this study, the researchers enrolled 50 consecutive women (median age 48 years; range, 21-75 years) who presented with a total of 60 BI-RADS 4 lesions. All patients underwent bilateral full-field mammography followed by sonographic exams.
In addition, before biopsy, subjects also underwent 1.5-tesla breast MRI scans, with the dedicated breast PET exam following. The researchers then fused MRI and dedicated breast PET images to better locate corresponding lesions and to avoid misinterpretation of dedicated breast PET results.
All findings were compared with histological results, which confirmed 42 benign lesions (70%) and 18 malignancies (30%). Of those malignancies, seven lesions (39%) were in situ and 11 (61%) were invasive carcinomas.
Dedicated breast PET discovered 19 lesions; 10 were benign and nine were malignant. However, the modality missed a significant number of carcinomas in situ and approximately one-fourth of invasive cancers. In other words, there were nine (50%) false-negative results in the sample.
| Performance of dedicated breast PET in BI-RADS 4 lesions |
|
| Sensitivity | 50% |
| Specificity | 76% |
| Percent of invasive carcinomas detected | 73% |
| Percent of in situ carcinomas detected | 14% |
"Two invasive carcinomas were located less than 1 cm from the pectoral muscle, which can explain that they were missed by dedicated breast PET, because they were outside the field-of-view," the authors noted.
Graña-López and colleagues recommended additional research with larger cohorts to better evaluate the role of dedicated breast PET with BI-RADS 4 lesions and "as a complementary tool to conventional breast imaging techniques."
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
