Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives

Romeo, Valeria; Accardo, Giuseppe; Perillo, Teresa; Basso, Luca; Garbino, Nunzia; Nicolai, Emanuele; Maurea, Simone; Salvatore, Marco

doi:10.3390/cancers13143521

Cited by 54 publications

(46 citation statements)

References 91 publications

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“…Pan’s study, implemented in China, differs from ours as their primary outcome was frozen status and included neoadjuvant chemotherapy (NAC) patients. The evidence shows that the imaging is significantly different and complex after preoperative treatment ( 27 , 28 ). As a result, the model including those patients would inevitably have limitations when applied to the general population.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prediction Model for Positive Margins in Breast-Conserving Surgery

2022

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BackgroundThe chances of second surgery due to positive margins in patients receiving breast-conversing surgery (BCS) were about 20-40%. This study aims to develop and validate a nomogram to predict the status of breast-conserving margins.MethodsThe database identified patients with core needle biopsy-proven ductal carcinoma in situ (DCIS) or invasive breast carcinoma who underwent BCS in Shanxi Bethune Hospital between January 1, 2015 and December 31, 2021 (n = 573). The patients were divided into two models: (1) The first model consists of 398 patients who underwent BCS between 2015 and 2019; (2) The validation model consists of 175 patients who underwent BCS between 2020 and 2021. The development of the nomogram was based on the findings of multivariate logistic regression analysis. Discrimination was assessed by computing the C-index. The Hosmer-Lemeshow goodness-of-fit test was used to validate the calibration performance.ResultsThe final multivariate regression model was developed as a nomogram, including blood flow signals (OR = 2.88, p = 0.001), grade (OR = 2.46, p = 0.002), microcalcifications (OR = 2.39, p = 0.003), tumor size in ultrasound (OR = 2.12, p = 0.011) and cerbB-2 status (OR = 1.99, p = 0.042). C-indices were calculated of 0.71 (95% CI: 0.64-0.78) and 0.68 (95% CI: 0.59-0.78) for the modeling and the validation group, respectively. The calibration of the model was considered adequate in the validation group (p > 0.05).ConclusionWe developed a nomogram that enables the estimation of the preoperative risk of positive BCS margins. Our nomogram provides a valuable tool for identifying high-risk patients who might have to undergo a wider excision.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prediction Model for Positive Margins in Breast-Conserving Surgery

2022

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“…Reports of radiomic analysis have shown importance in determining therapy response during NACT. Several studies used this radiomic analysis in the classification and prediction of treatment response with different settings based on the nature of the dataset and the performance calculations [9,[27][28][29][30].…”

Section: Discussionmentioning

confidence: 99%

Quantitative Analysis of Breast Cancer NACT Response on DCE-MRI Using Gabor Filter Derived Radiomic Features

Moyya

Mythili

2022

Int. J. Onl. Eng.

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In this work, an attempt has been made to quantify the treatment response due to Neoadjuvant Chemotherapy (NACT) on the publicly available QIN-Breast of TCIA database (N = 25) using Gabor filter derived radiomic features. The Gabor filter bank is constructed using 5 different scales and 7 different orientations. Different radiomic features were extracted from Gabor filtered Dynamic Contrast Enhanced Magnetic Resonance images (DCE-MRI) of patients having 3 different visits (Visit 1: before, Visit 2: after 1st cycle, and Visit 3: the last cycle of NACT). The extracted radiomic features were analyzed statistically and Area Under Receiver Operating Characteristic (AUROC) has been calculated. Results show that the Gabor derived radiomic features could differentiate the pathological differences among all three visits. Energy has shown a significant difference between all the three orientations particularly between Visits 2 & 3. However, Entropy from λ=2 and θ=300 between Visit 2 & 3, Skewness from λ=2 and θ=1200 between Visit 1 & 3 could differentiate the treatment response with high statistical significance of p=0.006 and 0.001 respectively. From the ROC analysis, the better predictors were Short Run Emphasis (SRE), Short Zone Emphasis (SZE), and Energy between Visit 1 & 3 by achieving an AUROC of 76.38%, 75.16%, and 71.10% respectively. Further, the results suggest that the radiomic features are capable of quantitatively compare the breast NACT prognosis that varies across multi-oriented Gabor filters.

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“…Ramana Kumari stated the changes in the spectrum of tumor bed are bizarre nuclei, necrosis, hyalinization, intense mono-nuclear collection of inflammatory cells and cytoplasmic vacuolization [12]. Chakrabarti also examined frequent morphological alterations in histological assessment were reduced cellularity, inflammatory cells bodies as 61.50%, fibrosis as 64.10% and necrosis as 30.80% [19,20].…”

Section: Discussionmentioning

confidence: 99%

Histo-Pathological Evaluation of Breast Cancer after Neo-Adjuvant Chemotherapy

Ullah¹,

Nasir²,

Hina³

et al. 2022

PJMHS

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Background: A recent standardized therapeutic technique for the patients having advanced and large (bigger than two centimeters) breast cancer is neo-adjuvant chemotherapy. The gold standard is the histo-pathological assessment of carcinoma response. Methodology: In this research study, we performed the histo-pathologic evaluation of forty-four samples received in the pathology department over a period of one year from 1 July 2021 to 30 June 2022. Results: In this research study, we studied total forty-four patients with breast cancer. Average age of these females was fifty years. Average size prior CT was 56.140 centimeter square and post CT was 29.4 centimeter square. In this research work, complete pathologic response was shown by eighteen percent patients and incomplete response was shown by eighty-four percent patients. Conclusion: Changes in pattern of characterizations examined after the histo-pathological evaluation of tumor are dystrophic calcification, stromal hyalinization, regions of coagulated necrosis, reactive infiltration of lymphocytes, cellular fibrous tissue and hemosiderin laden macrophages. Keywords: Characterization, pathology, evaluation, neo-adjuvant chemotherapy, carcinoma, cancer, average.

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Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives

Cited by 54 publications

References 91 publications

Development and Validation of a Prediction Model for Positive Margins in Breast-Conserving Surgery

Development and Validation of a Prediction Model for Positive Margins in Breast-Conserving Surgery

Quantitative Analysis of Breast Cancer NACT Response on DCE-MRI Using Gabor Filter Derived Radiomic Features

Histo-Pathological Evaluation of Breast Cancer after Neo-Adjuvant Chemotherapy

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