Superparamagnetic Iron Oxide (SPIO) nanoparticles serve as a promising tracer for sentinel lymph node (SLN) detection in breast cancer. Concerns exist regarding artefacts on postoperative Magnetic Resonance Imaging (MRI), especially following breast conservation (BCS). In this prospective observational study, 97 women with DCIS or invasive breast cancer underwent BCS and SLN detection with peritumoral SPIO injection administered up to four weeks before surgery. Postoperatively, patients were followed with MRI and mammograms, and imaging outcomes were evaluated by four, independent breast radiologists, blinded to clinical data, using a predefined, standardized questionnaire. In 97 patients included, there was inter-rater discordance in the prevalence of "any artefact" (range: 24.1-74.4 %; weighted average: 32.4 %) and "SPIO specific artefact" (range: 12.0-49.4 %; weighted average: 20.9 %). The median area of "any artefact" was 9.24 cm² (iqr 4.72, 15.50) and SPIO specific artefact 9.88 (iqr 5.32, 15.5). Likert scores indicated higher difficulty interpreting MRI if artefacts were present (OR: 2.295, 95 % CI 1.028, 5.123; p = 0.043), but this was reduced if intravenous contrast was administered (OR: 0.177, 95 % CI 0.091, 0.342; p < 0.001). Multivariable analysis identified free-hand SPIO administration as a risk factor (OR 8.929, 95 % CI 2.849, 27.778; p < 0.001). All six patients with local recurrence were successfully diagnosed on MRI by all raters. This prospective cohort study suggests that a targeted peritumoral SPIO injection can result in the removal of SPIO during lumpectomy and address the concerns for artefacts on postoperative MRI follow-up, in the selected patients that MRI may be warranted.

Vacuum-assisted excision (VAE) of benign breast lesions is increasingly adopted owing to its clinical performance and convenience. However, the potential complications, impact on patient experience, and healthcare costs need to be thoroughly addressed, which is the focus of this study. The Swedish VAE trial was a prospective, single-blind, randomised trial. The patients were randomised to 7G or 10G needle size groups, and VAE was performed under ultrasound or mammographic guidance. Patient acceptance of the method and short-term complications were documented 7 and 15 days after the procedure. Long-term follow-up examinations were performed at 6 and 24 months after the procedure. The total procedural costs were calculated and compared with those of open surgical excision (OSE), the previous standard of care for surgical procedures. In the trial, VAE was performed on 208 patients, of whom 178 answered a questionnaire regarding their experience. Among those, 87 and 91 had their procedure performed with a 7G and 10G needle, respectively. There were no significant differences in pain levels (p = 0.713), complications (p = 0.724), or patient acceptance of the procedure between the two groups (p = 0.401). Fifty-five per cent (98/178) of all patients reported pain levels < 2 on a visual -analogue scale ranging from 1 to 10, while 12 per cent (21/178) reported pain levels > 6. Ninety-seven per cent (173/178) of the patients would recommend the procedure to others. The total hospital procedural cost of VAE was estimated to be 60 % lower than the cost of OSE (p < 0.001). The VAE procedure using a 7G needle size was well -accepted by patients and conferred no increase in complications, including pain, compared to a 10G needle size. The VAE procedure had superior cost-efficiency compared to OSE.

In PREDIX LumB patients with estrogen receptor positive and human epidermal growth factor receptor negative (ER + /HER2-) breast cancer > 20 mm and/or with lymph node metastasis were randomized 1:1 to receive either paclitaxel weekly for 12 weeks followed by palbociclib and endocrine therapy for 12 weeks (arm A), or the reverse sequence (arm B). Primary endpoint is objective radiologic response at 12 weeks (ORR ₁₂ ), and key secondary endpoints are ORR ₂₄ , pathologic complete response, event-free survival, safety and correlative studies of tissue and circulating biomarkers. Whole exome sequencing and RNA sequencing were performed on baseline fresh frozen tissue samples. In total, 179 patients comprise the intention-to-treat population. There is no statistically significant difference between the two arms in ORR ₁₂ (59% vs 45%, p = 0.058). An exploratory gene expression analysis identified differentially expressed genes and gene sets between responders and non-responders at 12 weeks. A predictive signature, CDKPredX, comprising 31 genes related to proliferation, ER signaling and immune activity was developed to identify patients resistant to chemotherapy but responding to palbociclib plus endocrine therapy (p _interaction =0.03). The predictive signature was independently validated in the CORALLEEN trial (p _interaction =0.048). Clinicaltrials.gov identifier: NCT02603679

Background: Artificial intelligence (AI)–based cancer detectors (CAD) for mammography are starting to be used for breast cancer screening in radiology departments. It is important to understand how AI CAD systems react to benign lesions, especially those that have been subjected to biopsy. Objective: Our goal was to corroborate the hypothesis that women with previous benign biopsy and cytology assessments would subsequently present increased AI CAD abnormality scores even though they remained healthy. Methods: This is a retrospective study applying a commercial AI CAD system (Insight MMG, version 1.1.4.3; Lunit Inc) to a cancer-enriched mammography screening data set of 10,889 women (median age 56, range 40-74 years). The AI CAD generated a continuous prediction score for tumor suspicion between 0.00 and 1.00, where 1.00 represented the highest level of suspicion. A binary read (flagged or not flagged) was defined on the basis of a predetermined cutoff threshold (0.40). The flagged median and proportion of AI scores were calculated for women who were healthy, those who had a benign biopsy finding, and those who were diagnosed with breast cancer. For women with a benign biopsy finding, the interval between mammography and the biopsy was used for stratification of AI scores. The effect of increasing age was examined using subgroup analysis and regression modeling. Results: Of a total of 10,889 women, 234 had a benign biopsy finding before or after screening. The proportions of flagged healthy women were 3.5%, 11%, and 84% for healthy women without a benign biopsy finding, those with a benign biopsy finding, and women with breast cancer, respectively (P<.001). For the 8307 women with complete information, radiologist 1, radiologist 2, and the AI CAD system flagged 8.5%, 6.8%, and 8.5% of examinations of women who had a prior benign biopsy finding. The AI score correlated only with increasing age of the women in the cancer group (P=.01). Conclusions: Compared to healthy women without a biopsy, the examined AI CAD system flagged a much larger proportion of women who had or would have a benign biopsy finding based on a radiologist’s decision. However, the flagging rate was not higher than that for radiologists. Further research should be focused on training the AI CAD system taking prior biopsy information into account.

Papillary thyroid cancer is associated with lymph node metastasis and tumor recurrence that need repeated surgery, entailing surgical challenges with a high risk of complications such as nerve damage and bleeding. Magseed is a metal coil used to detect non-palpable lesions and is an established modality in breast cancer surgery. In this case series, we explore the feasibility of Magseed in metastasis surgery of papillary thyroid cancer. Under the guidance of ultrasonography, Magseed is injected into the target tissue and intraoperatively detected with a handheld magnetometer probe, Sentimag (R). Five patients with recurrence of papillary thyroid cancer were operated on with focused Magseed-guided localization. All patients had repeated surgery and radioiodine treatments. Four of the patients had lymph node metastasis hidden in the fibrosis, and one patient had recurrent tumor tissue on the left side of the larynx. Magseed was easy to use, safe, and precise in detecting the target tissue.

BACKGROUND Artificial intelligence (AI)–based cancer detectors (CAD) for mammography are starting to be used for breast cancer screening in radiology departments. It is important to understand how AI CAD systems react to benign lesions, especially those that have been subjected to biopsy. OBJECTIVE Our goal was to corroborate the hypothesis that women with previous benign biopsy and cytology assessments would subsequently present increased AI CAD abnormality scores even though they remained healthy. METHODS This is a retrospective study applying a commercial AI CAD system (Insight MMG, version 1.1.4.3; Lunit Inc) to a cancer-enriched mammography screening data set of 10,889 women (median age 56, range 40-74 years). The AI CAD generated a continuous prediction score for tumor suspicion between 0.00 and 1.00, where 1.00 represented the highest level of suspicion. A binary read (flagged or not flagged) was defined on the basis of a predetermined cutoff threshold (0.40). The flagged median and proportion of AI scores were calculated for women who were healthy, those who had a benign biopsy finding, and those who were diagnosed with breast cancer. For women with a benign biopsy finding, the interval between mammography and the biopsy was used for stratification of AI scores. The effect of increasing age was examined using subgroup analysis and regression modeling. RESULTS Of a total of 10,889 women, 234 had a benign biopsy finding before or after screening. The proportions of flagged healthy women were 3.5%, 11%, and 84% for healthy women without a benign biopsy finding, those with a benign biopsy finding, and women with breast cancer, respectively (P<.001). For the 8307 women with complete information, radiologist 1, radiologist 2, and the AI CAD system flagged 8.5%, 6.8%, and 8.5% of examinations of women who had a prior benign biopsy finding. The AI score correlated only with increasing age of the women in the cancer group (P=.01). CONCLUSIONS Compared to healthy women without a biopsy, the examined AI CAD system flagged a much larger proportion of women who had or would have a benign biopsy finding based on a radiologist’s decision. However, the flagging rate was not higher than that for radiologists. Further research should be focused on training the AI CAD system taking prior biopsy information into account.