Predicting Response to Neoadjuvant Chemotherapy Using 18F FDG PET-CT in Patients with Locally Advanced Breast Cancer

Hulikal, Narendra; Gajjala, Sivanath Reddy; Kalawat, Teckchand; Kadiyala, Silpa; Kottu, Radhika

doi:10.31557/apjcp.2020.21.1.93

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Regarding regional lymph node staging, FDG PET-CT has higher accuracy than other imaging techniques [ 58 ]. It was also demonstrated that the use of FDG PET-CT changed the initial staging and therapy and is helpful in the surveillance of neoadjuvant chemotherapy [ 58 , 60 ]. Using PET-CT after neoadjuvant chemotherapy can avoid radical mastectomy, detecting the presence of residual neoplasia better than MRI [ 61 ].…”

Section: Resultsmentioning

confidence: 99%

Breast Cancer—How Can Imaging Help?

et al. 2022

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Breast cancer is the most common malignant disease among women, causing death and suffering worldwide. It is known that, for the improvement of the survival rate and the psychological impact it has on patients, early detection is crucial. For this to happen, the imaging techniques should be used at their full potential. We selected and examined 44 articles that had as subject the use of a specific imaging method in breast cancer management (mammography, ultrasound, MRI, ultrasound-guided biopsy, PET-CT). After analyzing their data, we summarized and concluded which are the best ways to use each one of the mentioned techniques for a good outcome. We created a simplified algorithm with easy steps that can be followed by radiologists when facing this type of neoplasia.

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Section: Resultsmentioning

confidence: 99%

Breast Cancer—How Can Imaging Help?

et al. 2022

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“…In another study, Hulikal et al demonstrated that PET/CT was more accurate (87%) than clinical assessment (39%) and CT (56%) for response evaluation [ 32 ].…”

Section: Resultsmentioning

confidence: 99%

Innovations in Positron Emission Tomography and State of the Art in the Evaluation of Breast Cancer Treatment Response

Castorina,

Comis,

Prestifilippo

et al. 2023

JCM

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The advent of hybrid Positron Emission Tomography/Computed Tomography (PET/CT) and PET/Magnetic Resonance Imaging (MRI) scanners resulted in an increased clinical relevance of nuclear medicine in oncology. The use of [18F]-Fluorodeoxyglucose ([18F]FDG) has also made it possible to study tumors (including breast cancer) from not only a dimensional perspective but also from a metabolic point of view. In particular, the use of [18F]FDG PET allowed early confirmation of the efficacy or failure of therapy. The purpose of this review was to assess the literature concerning the response to various therapies for different subtypes of breast cancer through PET. We start by summarizing studies that investigate the validation of PET/CT for the assessment of the response to therapy in breast cancer; then, we present studies that compare PET imaging (including PET devices dedicated to the breast) with CT and MRI, focusing on the identification of the most useful parameters obtainable from PET/CT. We also focus on novel non-FDG radiotracers, as they allow for the acquisition of information on specific aspects of the new therapies.

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“…This shows that these parameters should always be taken into account for the construction of reliable predictive models, because of their well-known importance. Most of the PET/CT studies for the prediction of the treatment response in breast cancer do not include radiomic analysis, and they have been performed in a neoadjuvant context [ 34 , 35 ]; only a few studies consider the treatment response in patients with metastatic cancer [ 6 , 36 ]. On the other hand, concerning the existing PET/CT radiomic studies in breast cancer, they have only considered a neoadjuvant context [ 37 , 38 , 39 , 40 ].…”

Section: Discussionmentioning

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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Predicting Response to Neoadjuvant Chemotherapy Using 18F FDG PET-CT in Patients with Locally Advanced Breast Cancer

Cited by 7 publications

References 20 publications

Breast Cancer—How Can Imaging Help?

Breast Cancer—How Can Imaging Help?

Innovations in Positron Emission Tomography and State of the Art in the Evaluation of Breast Cancer Treatment Response

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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