Molecular Dynamics (MD) simulations of propane dimer in different solvents (water, acetonitrile and methanol) were performed by using CHARMM platform for modeling the solute and solvents. A series of ...Umbrella sampling MD simulations were carried out in each solvent separately and potential of mean force (PMFs) were calculated by using Weighted Histogram Analysis Method. Results show that two minima (contact minima and solvent separated minima) characterize the PMF of propane dimer in all three solvent environments. The contact minima are deeper and less sensitive to solvent environment for its position. However, significant effect in the position of second minima, solvent separated minima, was observed. Our study reveals that the interaction between propane dimer is softer in methanol and acetonitrile than in water.
BIBECHANA 17 (2020) 1-12
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients ...achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.
Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective ...treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (
< 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82,
< 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.
Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects ...associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.
Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model ...of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.
Molecular Dynamics (MD) simulations of propane dimer in different solvents (water, acetonitrile and methanol) were performed by using CHARMM platform for modeling the solute and solvents. A series of ...Umbrella sampling MD simulations were carried out in each solvent separately and potential of mean force (PMFs) were calculated by using Weighted Histogram Analysis Method. Results show that two minima (contact minima and solvent separated minima) characterize the PMF of propane dimer in all three solvent environments. The contact minima are deeper and less sensitive to solvent environment for its position. However, significant effect in the position of second minima, solvent separated minima, was observed. Our study reveals that the interaction between propane dimer is softer in methanol and acetonitrile than in water. BIBECHANA 17 (2020) 1-12
Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and ...could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST).
To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST.
Prospective.
Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR).
3.0 T/reduced field of view single-shot echo-planar DTI sequence.
Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery.
Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant.
47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm
/s).
Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC.
1 TECHNICAL EFFICACY: Stage 4.
Abstract Purpose: Neoadjuvant immunotherapy (NIT) in combination with neoadjuvant chemotherapy (NCT) was recently approved for treatment of TNBC patients with increased rates of pathologic complete ...response (pCR) compared to NCT alone. The aim of this study was to evaluate if dynamic contrast-enhanced (DCE)-MRI performed after 2 and/or 4 cycles of NIT + NCT, can predict which patients will achieve pCR, potentially triaging them to continuation of NIT+NCT or, when appropriate, to de-escalation trials. Alternatively, identified chemoresistant tumors who are unlikely to achieve pCR may be directed to other treatment strategies, including novel targeted trials, and avoid the unnecessary toxicity of NIT. Methods and Materials: Preliminary analysis included 64 patients from prospective IRB-approved study (NCT02276443) with stage I-III TNBC who underwent DCE-MRI at baseline (BL), after 2 cycles (C2), and 4 cycles (C4) of NIT combined with standard of care NCT (Paclitaxel +/- carboplatin). Tumor volumes were calculated using 3 axis measurements of the index lesion at the DCE MRI and percent tumor volume reduction (TVR) between BL, C2, and C4 was calculated. pCR was assessed at surgery after completion of neoadjuvant treatment. Correlation between pCR and TVR was evaluated using ROC analysis. Results: 59% (38/64) of TNBC patients achieved pCR after NIT+NCT. DCE-MRI after 2 cycles of NIT+NCT was able to predict pCR with an AUC of 0.71 (95% CI: 0.57-0.84). TVR >90% at C2 predicted pCR with PPV 86%, and TVR < 35% predicted chemoresistance with NPV 100%. Following 4 cycles of treatment DCE-MRI was able to predict pCR with an AUC of 0.81 (95% CI: 0.69-0.92). TVR >95% at C4 was predictive of chemosensitivity with PPV 82%, while TVR < 75% was predictive of chemoresistance with NPV 100%. Conclusions: DCE-MRI volumetric changes early during NIT + NCT were able to predict pCR status of TNBC patients as either excellent responders or nonresponders, triaging them to SOC neoadjuvant therapy with option for de-escalation trials, or targeted therapies, respectively. These preliminary results will be validated in the larger cohort after completion of the ongoing prospective clinical trial. Citation Format: Gaiane Rauch, Mary Guirguis, Miral Patel, Rosalind Candelaria, Rania Mohamed, Tanya Moseley, H. T. Carisa Le-Petross, Jessica Leung, Gary Whitman, Deanna Lane, Marion Scoggins, Frances Perez, Jia Sun, Sanaz Pashapoor, Zhan Xu, Jason White, Peng Wei, Brandy Reed, Jong Bum Son, Ken-Pin Hwang, Bikash Panthi, Anil Korkut, Lei Huo, Kelly Hunt, Alyson Clayborn, Jennifer Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Wei Yang, Jingfei Ma, Beatriz Adrada. Early prediction of response to Neoadjuvant Immunotherapy in Triple Negative Breast Cancer (TNBC) with DCE-MRI abstract. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS05-07.
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