18F‑FDG PET/CT for the early prediction of the response rate and survival of patients with recurrent or metastatic breast cancer

Zhang, Feng‐Chun; Xu, Haiyan; Liu, Jianjun; Xu, Yuanfan; Chen, Bin; Yang, Yijin; Yan, Ningning; Song, Shaoli; Lin, Yanhong; Xu, Yingchun

doi:10.3892/ol.2018.9151

Cited by 8 publications

(11 citation statements)

References 35 publications

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“…These patterns are in line with the findings of our study to some degree and could be explained by the inability of CE-CT to distinguish osteoblastic bone lesions from bone healing. They also showed that FDG-PET/CT could be a better predictor for progression-free survival and disease-specific survival compared to CE-CT [17], which was proposed in some other studies as well [24,25]. These results indicate that FDG-PET/CT may be superior to CE-CT in the response assessment of MBC patients [17,24,25].…”

Section: Discussionmentioning

confidence: 73%

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Clinical Impact of FDG-PET/CT Compared with CE-CT in Response Monitoring of Metastatic Breast Cancer

Naghavi-Behzad

Oltmann

Alamdari

et al. 2021

Cancers

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We compared response categories and impacts on treatment decisions for metastatic breast cancer (MBC) patients that are response-monitored with contrast-enhanced computed-tomography (CE-CT) or fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT). A comparative diagnostic study was performed on MBC patients undergoing response monitoring by CE-CT (n = 34) or FDG-PET/CT (n = 31) at the Odense University Hospital (Denmark). The responses were assessed visually and allocated into categories of complete response (CR/CMR), partial response (PR/PMR), stable disease (SD/SMD), and progressive disease (PD/PMD). Response categories, clinical impact, and positive predictive values (PPV) were compared for follow-up scans. A total of 286 CE-CT and 189 FDG-PET/CT response monitoring scans were performed. Response categories were distributed into CR (3.8%), PR (8.4%), SD (70.6%), PD (15%), and others (2.1%) by CE-CT and into CMR (22.2%), PMR (23.8%), SMD (31.2%), PMD (18.5%), and others (4.4%) by FDG-PET/CT, revealing a significant difference between the groups (P < 0.001). PD and PMD caused changes of treatment in 79.1% and 60%, respectively (P = 0.083). PPV for CE-CT and FDG-PET/CT was 0.85 (95% CI: 0.72–0.97) and 0.70 (95% CI: 0.53–0.87), respectively (P = 0.17). FDG-PET/CT indicated regression of disease more frequently than CE-CT, while CE-CT indicated stable disease more often. FDG-PET/CT seems to be more sensitive than CE-CT for monitoring response in metastatic breast cancer.

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Section: Discussionmentioning

confidence: 73%

“…They also showed that FDG-PET/CT could be a better predictor for progression-free survival and disease-specific survival compared to CE-CT [17], which was proposed in some other studies as well [24,25]. These results indicate that FDG-PET/CT may be superior to CE-CT in the response assessment of MBC patients [17,24,25].…”

Section: Discussionmentioning

confidence: 73%

Clinical Impact of FDG-PET/CT Compared with CE-CT in Response Monitoring of Metastatic Breast Cancer

Naghavi-Behzad

Oltmann

Alamdari

et al. 2021

Cancers

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“…14 Diagnosis of pleural metastasis is essential considering that the most common pleural malignancy is the metastatic involvement in which high incidence causative primary diseases are bronchogenic carcinoma, breast carcinoma and lymphoma. 15,16 PET/CT scan is an efficient imaging method for initial staging, assessment of treatment response and evaluation of recurrence by quantitating FDG avidity of cancer with standardized uptake value. Limited by low sensitivity to detect breast cancers smaller than 1 cm and lobular carcinomas, PET/CT still has efficacy superior to convential imaging for the locoregional spread and distant metastasis.…”

Section: Discussionmentioning

confidence: 99%

“…Limited by low sensitivity to detect breast cancers smaller than 1 cm and lobular carcinomas, PET/CT still has efficacy superior to convential imaging for the locoregional spread and distant metastasis. [15][16][17][18] Moreover, PET/CT is considered to be more valuable in diagnosing skeletal metastasis compared with bone scintigraphy and predicting recurrence. [18][19][20][21][22][23][24] This study distinctly states that absence of FDG uptake in pleura or pleural effusion minimizes the risk of metastasis regarding that none of 15 patients with no avidity in PET/CT were diagnosed with a pleural malignancy.…”

Section: Discussionmentioning

confidence: 99%

F-18 fluorodeoxyglucose positron emission computed tomography in the diagnosis of pleural metastases from breast carcinoma

Sarıçam¹

2020

Int Surg J

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Background: This study aimed to investigate the feasibility of F-18 fluorodeoxyglucose (FDG) positron emission computed tomography (PET/CT) in identifying the pleural invasion of metastatic breast cancers.Methods: A retrospective study was conducted to include 75 patients with untreated breast cancer who had undergone thoracoscopy to drain pleural effusions and to perform pleural biopsies. Whole group of patients were evaluated in terms of age, type of primary breast cancer, macroscopic appearance of pleura during thoracoscopy, maximum standardized FDG uptake value (SUV) reported by PET/CT scan in addition to presence of malignancy detected in pleura and/or pleural effusion.Results: All of 75 patients were female and mean age was 56.12±11.70. Metastatic disease was diagnosed in the pleura of 40 (53.3%) and in the pleural effusion of 43 (57.3%) patients. The sensitivity and specificity of PET/CT in detecting pleural metastases of breast carcinoma was calculated as 88.2% and 96.2% whereas PET/CT demonstrated sensitivity of 91.9% and specificity of 91.3% in identifying malignant pleural effusion. Cut-off values of FDG uptake were 4.25 for pleural metastases and 3.85 for malignant pleural effusions. PET/CT also indicated a false negative rate of 12.5%, a false positive rate of 16.28% and an overall accuracy rate of 85.33% in the diagnosis of pleural metastasis of breast carcinoma.Conclusions: PET/CT reporting an FDG uptake over 4 in the pleura or pleural effusion is beneficial in managing the patients with the suspicion of pleural metastases from breast cancer.

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“…Changes in tumor metabolic activity, assessed by [ 18 F]F-FDG PET/CT, are an early indicator of treatment effectiveness for breast cancer, in both neoadjuvant and metastatic settings [ 5 ]. Likewise, a significant reduction in the metabolic activity of the tumor (i.e., treatment-sensible tumors), early during therapy, is associated with longer overall survival and progression-free survival in this tumor [ 6 ]. However, in metastatic disease, the post-therapeutic response assessment frequently shows heterogeneous responses, which means that responding and nonresponding metastatic lesions coexist (dissociated response) [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

Gómez

Herraiz

Udı́as

et al. 2022

Cancers

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Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [18F]F-FDG PET/CT images. Methods: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [18F]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized uptake values normalized for body lean body mass). The lesion pool was divided into training (n = 182) and testing cohorts (n = 46); for each lesion, 101 image features from both PET and CT were extracted (202 features per lesion). These features, along with clinical and pathological information, allowed the prediction model’s construction by using seven popular feature selection methods in cross-combination with another seven machine-learning (ML) classifiers. The performance of the different models was investigated with the receiver-operating characteristic curve (ROC) analysis, using the area under the curve (AUC) and accuracy (ACC) metrics. Results: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 ± 0.06 and 0.92 ± 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts. Conclusions: Image features obtained from a pretreatment [18F]F-FDG PET/CT along with clinical vaiables could predict the metabolic response of metastatic breast cancer lesions, by their incorporation into predictive models, whose performance depends on the selected combination between feature selection and ML classifier methods.

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18F‑FDG PET/CT for the early prediction of the response rate and survival of patients with recurrent or metastatic breast cancer

Cited by 8 publications

References 35 publications

Clinical Impact of FDG-PET/CT Compared with CE-CT in Response Monitoring of Metastatic Breast Cancer

Clinical Impact of FDG-PET/CT Compared with CE-CT in Response Monitoring of Metastatic Breast Cancer

F-18 fluorodeoxyglucose positron emission computed tomography in the diagnosis of pleural metastases from breast carcinoma

Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

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