PURPOSE We evaluated the addition of breast magnetic resonance imaging (MRI) to standard radiologic evaluation on the re-intervention rate in women with ductal carcinoma in situ (DCIS) undergoing breast-conserving surgery. PATIENTS AND METHODS Women with biopsy-proven DCIS corresponding to a unifocal microcalcification cluster or a mass less than 30 mm were randomly assigned to undergo MRI or standard evaluation. The primary end point was the re-intervention rate for positive or close margins (< 2 mm) in the 6 months after randomization ( ClinicalTrials.gov identifier: NCT01112254). RESULTS A total of 360 patients from 10 hospitals in France were included in the study. Of the 352 analyzable patients, 178 were randomly assigned to the MRI arm, and 174 were assigned to the control arm. In the intent-to-treat analysis, 82 of 345 patients with the assessable end point were reoperated for positive or close margins within 6 months, resulting in a re-intervention rate of 20% (35 of 173) in the MRI arm and 27% (47 of 172) in the control arm. The absolute difference of 7% (95% CI, −2% to 16%) corresponded to a relative reduction of 26% (stratified odds ratio, 0.68; 95% CI, 0.41 to 1.1; P = .13). When considering only the per-protocol population with an assessable end point, the difference was 9% (stratified odds ratio, 0.59; 95% CI, 0.35 to 1.0; P = .05). Total mastectomy rates were 18% (31 of 176) in the MRI arm and 17% (30 of 173) in the control arm (stratified P = .93). For 100 lesions seen on MRI, nonmass-like enhancement was more predominant (82%) than mass enhancement (20%). Nevertheless, no specific morphologic and kinetic parameters for DCIS were identified. CONCLUSION The study did not show sufficient surgical improvement with the use of preoperative MRI to be clinically relevant in DCIS staging. However, this could be reconsidered with the improvement of new MRI sequences and new modalities in magnetic resonance techniques.
A white blood cell count more than 50 · 10 9 /l, not related to bone marrow involvement, is termed leukemoid reaction. We report on the first case of an undifferentiated sarcoma of the lung associated with an intense paraneoplastic neutrophilic leukemoid reaction related to the production of granulocyte colony-stimulating factor (G-CSF). A radiography and a computed tomography scan of the chest revealed a well-limited voluminous and heterogeneous low-density mass of the left lung. The patient died of multiorgan failure related to uncontrolled progressive tumor growth after admission and two cycles of chemotherapy. The patient's G-CSF serum concentration was dramatically elevated (6,538 pg/ml) compared to serum levels observed in normal controls and patients with elevated leukocytosis (31 and 387 pg/ml, respectively). The G-CSF concentration dramatically increased after the first cycle of chemotherapy and during the subsequent neutropenia, as a result of the tumor lyses as well as of disruption of the physiological negative feedback mechanism. Adjunction of the patient's serum to CD34+ cell cultures induced a 12.3-fold increase in CD15+ cells, demonstrating the serum's capacity to induce myeloid differentiation. Am. J. Hematol. 80:243-245, 2005. Key words: G-CSF; leukemoid reaction; sarcoma A white blood cell count more than 50 Â 10 9 /l not related to bone marrow involvement, termed leukemoid reaction, is observed in a wide variety of clinical settings. We report on a case of an undifferentiated sarcoma of the lung associated with an intense paraneoplastic neutrophilic leukemoid reaction related to the production of granulocyte colony-stimulating factor (G-CSF).A 54-year-old man was admitted in July 2003 because of anorexia, weight loss, cough, and weakness. Physical examination revealed large hepatomegaly and splenomegaly. Three months before admission, the patient developed an elevated white blood cell (WBC) count to 130Â 10 9 /l with 96% neutrophils, 1% lymphocytes, 1% monocytes, and 2% metamyelocytes. Hemoglobin was at 8 g/dl, and the platelet count was 191Â10 9 /l. A bone marrow biopsy specimen showed increased cellularity, with dramatic expansion of neutrophilic granulopoiesis. Karyotype analysis showed no evidence of clonal abnormality, and no bcr-abl fusion transcripts were detected. A diagnosis of chronic neutrophilic leukemia (CNL) was first proposed. Hydroxyurea was unsuccessfully administered during 2 months. Radiography and a computed tomography (CT) scan of the chest revealed a voluminous mass of the left lung (Fig. 1A). Bronchial endoscopy showed extrinsic compression of the upper left bronchus and a tumor of the culminal division. Biopsies revealed a proliferation characterized by a malignant
NCB for large breast tumors allowed reliable determination of ER/PR expression. However, the SBR grade may be deeply underestimated, and false-negative evaluation of the HER2 status would have led to a detrimental lack of trastuzumab administration.
Figure 4. Magnetic resonance image. Cystic lymphangioma of the left breast (hypersignal T2).
Objective To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM). Methods This retrospective mono-centric study included biopsy-proven invasive cancers with an enhancement on CESM. CESM images include low-energy images (LE) comparable to digital mammography and dual-energy subtracted images (DES) showing tumour angiogenesis. For each lesion, histologic type, tumour grade, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, Ki-67 proliferation index, and the size of the invasive tumour were retrieved. The deep learning model used was a CheXNet-based model fine-tuned on CESM dataset. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for the different models: images by images and then by majority voting combining all the incidences for one tumour. Results In total, 447 invasive breast cancers detected on CESM with pathological evidence, in 389 patients, which represented 2460 images analysed, were included. Concerning the ER, the deep learning model on the DES images had an AUC of 0.83 with the image-by-image analysis and of 0.85 for the majority voting. For the triple-negative analysis, a high AUC was observable for all models, in particularity for the model on LE images with an AUC of 0.90 for the image-by-image analysis and 0.91 for the majority voting. The AUC for the other histoprognostic factors was lower. Conclusion Deep learning analysis on CESM has the potential to determine histoprognostic tumours makers, notably estrogen receptor status, and triple-negative receptor status. Key Points • A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography. • The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors. • Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08538-4.
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