UNLABELLED: Combined therapies represent a staple of modern medicine. For women treated with neoadjuvant chemotherapy (NA ChT) for locally advanced breast cancer (LABC), early determination of whether the patient will fail to respond can enable the use of alternative, more beneficial therapies. This is even more desirable when the combined therapy includes hyperthermia (HT), an efficient way to improve drug delivery, however, more costly and time consuming. There is data showing that this goal can be achieved using magnetic resonance imaging (MRI) with contrast agent (CA) enhancement. This work for the first time proposes combining the information extracted from pre-treatment MR imaging into a morpho-physiological tumour score (MPTS) with the hypothesis that this score will increase the prognostic efficacy, compared to each of its MR-derived components: morphological (derived from the shape of the tumour enhancement) and physiological (derived from the CA enhancement variance dynamics parameters). The MPTS was correlated with response as determined by both pathologic residual tumour and MRI imaging, and was shown to have potential to predict response. The MPTS was extracted from pre-treatment MRI parameters, so independent of the combined therapy used. PURPOSE: To use a novel morpho-physiological tumour score (MPTS) generated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict response to treatment. MATERIALS AND METHODS: A protocol was designed to acquire DCE-MRI images of 20 locally advanced breast cancer (LABC) patients treated with neoadjuvant chemotherapy (NA ChT) and hyperthermia (HT). Imaging was done over 30 min following bolus injection of gadopentetate-based contrast agent. Parametric maps were generated by fitting the signal intensity to a double exponential curve and were used to derive a morphological characterisation of the lesions. Enhancement-variance dynamics parameters, wash-in and wash-out parameters (WiP, WoP), were extracted. The morphological characterisation and the WiP and WoP were combined into a MPTS with the intent of achieving better prognostic efficacy. The MPTS was correlated with response to NA therapy as determined by pathological residual tumour and MRI imaging. RESULTS: The contrast agent in all tumours typically peaked in the first 1-4 min. The tumours' WiP and WoP varied considerably. The MPTS was highly correlated with whether the patients had a pathological response. This scoring system has a specificity of 78% and a sensitivity of 91% for predicting response to NA chemotherapy. The kappa was 0.69 with a 95% confidence interval of [0.38, 1] and a p-value of 0.002. CONCLUSIONS: This pilot study shows that the MPTS derived using pre-treatment MRI images has the potential to predict response to NA ChT and HT in LABC patients. Further prospective studies are needed to confirm the validity of these results.
UNLABELLED: Combined therapies represent a staple of modern medicine. For women treated with neoadjuvant chemotherapy (NA ChT) for locally advanced breast cancer (LABC), early determination of whether the patient will fail to respond can enable the use of alternative, more beneficial therapies. This is even more desirable when the combined therapy includes hyperthermia (HT), an efficient way to improve drug delivery, however, more costly and time consuming. There is data showing that this goal can be achieved using magnetic resonance imaging (MRI) with contrast agent (CA) enhancement. This work for the first time proposes combining the information extracted from pre-treatment MR imaging into a morpho-physiological tumour score (MPTS) with the hypothesis that this score will increase the prognostic efficacy, compared to each of its MR-derived components: morphological (derived from the shape of the tumour enhancement) and physiological (derived from the CA enhancement variance dynamics parameters). The MPTS was correlated with response as determined by both pathologic residual tumour and MRI imaging, and was shown to have potential to predict response. The MPTS was extracted from pre-treatment MRI parameters, so independent of the combined therapy used. PURPOSE: To use a novel morpho-physiological tumour score (MPTS) generated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict response to treatment. MATERIALS AND METHODS: A protocol was designed to acquire DCE-MRI images of 20 locally advanced breast cancer (LABC) patients treated with neoadjuvant chemotherapy (NA ChT) and hyperthermia (HT). Imaging was done over 30 min following bolus injection of gadopentetate-based contrast agent. Parametric maps were generated by fitting the signal intensity to a double exponential curve and were used to derive a morphological characterisation of the lesions. Enhancement-variance dynamics parameters, wash-in and wash-out parameters (WiP, WoP), were extracted. The morphological characterisation and the WiP and WoP were combined into a MPTS with the intent of achieving better prognostic efficacy. The MPTS was correlated with response to NA therapy as determined by pathological residual tumour and MRI imaging. RESULTS: The contrast agent in all tumours typically peaked in the first 1-4 min. The tumours' WiP and WoP varied considerably. The MPTS was highly correlated with whether the patients had a pathological response. This scoring system has a specificity of 78% and a sensitivity of 91% for predicting response to NA chemotherapy. The kappa was 0.69 with a 95% confidence interval of [0.38, 1] and a p-value of 0.002. CONCLUSIONS: This pilot study shows that the MPTS derived using pre-treatment MRI images has the potential to predict response to NA ChT and HT in LABC patients. Further prospective studies are needed to confirm the validity of these results.
Authors: D M Ikeda; N M Hylton; K Kinkel; M G Hochman; C K Kuhl; W A Kaiser; J C Weinreb; S F Smazal; H Degani; P Viehweg; J Barclay; M D Schnall Journal: J Magn Reson Imaging Date: 2001-06 Impact factor: 4.813
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Authors: Subramani Mani; Yukun Chen; Lori R Arlinghaus; Xia Li; A Bapsi Chakravarthy; Sandeep R Bhave; E Brian Welch; Mia A Levy; Thomas E Yankeelov Journal: AMIA Annu Symp Proc Date: 2011-10-22
Authors: Michael J Flister; Shirng-Wern Tsaih; Alexander Stoddard; Cody Plasterer; Jaidip Jagtap; Abdul K Parchur; Gayatri Sharma; Anthony R Prisco; Angela Lemke; Dana Murphy; Mona Al-Gizawiy; Michael Straza; Sophia Ran; Aron M Geurts; Melinda R Dwinell; Andrew S Greene; Carmen Bergom; Peter S LaViolette; Amit Joshi Journal: Breast Cancer Res Treat Date: 2017-05-31 Impact factor: 4.872