Less Is More: The Evolving Surgical Approach to Breast Cancer

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Simple Summary

Accurate clinical axillary evaluation plays an important role in the diagnosis of and treatment planning for breast cancer (BC). This study aimed to develop a machine learning model integrating dedicated breast PET and clinical characteristics for prediction of axillary lymph node status in cT1-2N0-1M0 BC non-invasively. The performance of this integrating model in identifying pN0 and pN1 with the AUC was 0.94. We achieved an NPV of 96.88% in the cN0 and PPV of 92.73% in the cN1 subgroup. The higher true positive and true negative rate could delineate clinical subtypes and apply more precise treatment for patients with early-stage BC.

Abstract

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (¹⁸F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

Diagnostic performance of a novel high-resolution dedicated axillary PET system in the assessment of regional nodal spread of disease in early breast cancer

Background

In early breast cancer, a non-invasive method with higher sensitivity and negative predictive value (NPV) is needed to identify and recognize more indolent axillary lymph nodes (ALNs). This study aimed to assess whether a novel high-resolution dedicated ALN positron emission tomography (LymphPET) system could improve sensitivity in detecting early breast cancer (clinical N0-N1 stage).

Methods

A total of 103 patients with clinical stage T1-2N0-1M0 breast cancer were evaluated by ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) LymphPET. The maximum single-voxel PET uptake value of ALNs (maxLUV) and the tumor-to-background ratio (TBR) for fat (TBR1) and muscle (TBR2) tissue were calculated. Then, 78 patients with cN0 stage breast cancer received sentinel lymph node biopsy alone or combined with axillary lymph node dissection (ALND), and 25 patients with cN1 stage breast cancer underwent fine-needle aspiration.

Results

A total of 99 invasive breast carcinoma cases were included in this study. The diagnostic sensitivity of LymphPET was 88%, specificity was 79%, false-negative rate was 12%, the false-positive rate was 21%, positive predictive value was 75%, NPV was 90%, and accuracy was 83%. The maxLUV was superior to TBR1 and TBR2 in detecting ALNs, with 0.27 being the most optimal cutoff value.

Conclusions

The ¹⁸F-FDG LymphPET system can be used to identify and recognize more indolent ALNs of breast cancer due to greater sensitivity and a much higher NPV.

Digital Mammography Has Persistently Increased High-Grade and Overall DCIS Detection Without Altering Upgrade Rate

OBJECTIVE. The purpose of this article is to evaluate whether digital mammography (DM) is associated with persistent increased detection of ductal carcinoma in situ (DCIS) or has altered the upgrade rate of DCIS to invasive cancer. MATERIALS AND METHODS. An institutional review board-approved retrospective search identified DCIS diagnosed in women with mammographic calcifications between 2001 and 2014. Ipsilateral cancer within 2 years, masses, papillary DCIS, and patients with outside imaging were excluded, yielding 484 cases. Medical records were reviewed for mammographic calcifications, technique, and pathologic diagnosis. Mammograms were interpreted by radiologists certified by the Mammography Quality Standards Act. The institution transitioned from film-screen mammography (FSM) to exclusive DM by 2010. Statistical analyses were performed using chi-square test. RESULTS. Of 484 DCIS cases, 158 (33%) were detected by FSM and 326 (67%) were detected by DM. The detection rate was higher with DM than FSM (1.4 and 0.7 per 1000, respectively; p < .001). The detection rate of high-grade DCIS doubled with DM compared with FSM (0.8 and 0.4 per 1000, respectively; p < .001). The prevalent peak of DM-detected DCIS was 2.7 per 1000 in 2008. Incident DM detection remained double FSM (1.4 vs 0.7 per 1000). Similar proportions of high-grade versus low- to intermediate-grade DCIS were detected with both modalities. There was no significant difference in the upgrade rate of DCIS to invasive cancer between DM (10%; 34/326) and FSM (10%; 15/158) (p = .74). High-grade DCIS led to 71% (35/49) of the upgrades to invasive cancer. CONCLUSION. DM was associated with a significant doubling in DCIS and high-grade DCIS detection, which persisted after prevalent peak. The majority of upgrades to invasive cancer arose from high-grade DCIS. DM was not associated with decreased upgrade to invasive cancer.

Screening detects a myriad of breast disease - refining practice will increase effectiveness and reduce harm

For many individuals, the term 'cancer' equates to a disease that if untreated will progress, spread from the area initially affected and ultimately cause death. 'Breast cancer', however, is a diverse of range of pathological entities, incorporating indolent to fast-growing and aggressive lesions, with varying histological patterns, clinical presentations, treatment responses and outcomes. Screening for malignancy is based on the assumption that cancer has a gradual, orderly progression and that detecting lesions earlier in their natural history, and intervening, will reduce mortality. The natural history of epithelial atypia, ductal carcinoma in situ and even invasive breast cancer is poorly understood, but widely variable. We believe that population breast screening methodology needs to change to focus on diagnosis of lesions of greatest clinical relevance.