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Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results.

ABSTRACT: BACKGROUND:Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting. METHODS:Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher's linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models. RESULTS:Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively. CONCLUSION:QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.

SUBMITTER: Quiaoit K

PROVIDER: S-EPMC7384762 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results.

Quiaoit Karina K DiCenzo Daniel D Fatima Kashuf K Bhardwaj Divya D Sannachi Lakshmanan L Gangeh Mehrdad M Sadeghi-Naini Ali A Dasgupta Archya A Kolios Michael C MC Trudeau Maureen M Gandhi Sonal S Eisen Andrea A Wright Frances F Look-Hong Nicole N Sahgal Arjun A Stanisz Greg G Brezden Christine C Dinniwell Robert R Tran William T WT Yang Wei W Curpen Belinda B Czarnota Gregory J GJ

PloS one 20200727 7

<h4>Background</h4>Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting.<h4>Methods</h4>Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA ...[more]

PMID: 32716959

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Project description:PurposeTNFerade biologic is a novel means of delivering tumor necrosis factor alpha to tumor cells by gene transfer. We herein report final results of the largest randomized phase III trial performed to date among patients with locally advanced pancreatic cancer (LAPC) and the first to test gene transfer against this malignancy.Patients and methodsIn all, 304 patients were randomly assigned 2:1 to standard of care plus TNFerade (SOC + TNFerade) versus standard of care alone (SOC). SOC consisted of 50.4 Gy in 28 fractions with concurrent fluorouracil (200 mg/m(2) per day continuous infusion). TNFerade was injected intratumorally before the first fraction of radiotherapy each week at a dose of 4 × 10(11) particle units by using either a percutaneous transabdominal or an endoscopic ultrasound approach. Four weeks after chemoradiotherapy, patients began gemcitabine (1,000 mg/m(2) intravenously) with or without erlotinib (100 to 150 mg per day orally) until progression or toxicity.ResultsThe analysis included 187 patients randomly assigned to SOC + TNFerade and 90 to SOC by using a modified intention-to-treat approach. Median follow-up was 9.1 months (range, 0.1 to 50.5 months). Median survival was 10.0 months for patients in both the SOC + TNFerade and SOC arms (hazard ratio [HR], 0.90; 95% CI, 0.66 to 1.22; P = .26). Median progression-free survival (PFS) was 6.8 months for SOC + TNFerade versus 7.0 months for SOC (HR, 0.96; 95% CI, 0.69 to 1.32; P = .51). Among patients treated on the SOC + TNFerade arm, multivariate analysis showed that TNFerade injection by an endoscopic ultrasound-guided transgastric/transduodenal approach rather than a percutaneous transabdominal approach was a risk factor for inferior PFS (HR, 2.08; 95% CI, 1.06 to 4.06; P = .032). The patients in the SOC + TNFerade arm experienced more grade 1 to 2 fever and chills than those in the SOC arm (P < .001) but both arms had similar rates of grade 3 to 4 toxicities (all P > .05).ConclusionSOC + TNFerade is safe but not effective for prolonging survival in patients with LAPC.

Project description:PurposeTo develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images.MethodsOver 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant).ResultsThe majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96).ConclusionsThe proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.

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Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results.

Publications

Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results.

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