Search

Abstract Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model. This retrospective study included 304 EBC patients recruited from multiple centers. All enrollees had completed NACT regimens, and underwent US examinations at baseline and at each NACT cycle. We subsequently determined that percentage reduction of tumor maximum diameter from baseline to third cycle of NACT serves to independent predictor for pCR, enabling creation of a nomogram ( $$\text{AUC}=0.75$$ ). Our predictive accuracy further improved ( $$\text{AUC}=0.868$$ ) by combining dynamic US data and clinicopathological features in a machine learning model. Such models may offer a means of accurately predicting NACT responses in this setting, helping to individualize patient therapy. Our study may provide additional insights into the US-based response prediction by focusing on the dynamic changes of the tumor in the early and full NACT cycle.

Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model. This retrospective study included 304 EBC patients recruited from multiple centers. All enrollees had completed NACT regimens, and underwent US examinations at baseline and at each NACT cycle. We subsequently determined that percentage reduction of tumor maximum diameter from baseline to third cycle of NACT serves to independent predictor for pCR, enabling creation of a nomogram (). Our predictive accuracy further improved () by combining dynamic US data and clinicopathological features in a machine learning model. Such models may offer a means of accurately predicting NACT responses in this setting, helping to individualize patient therapy. Our study may provide additional insights into the US-based response prediction by focusing on the dynamic changes of the tumor in the early and full NACT cycle. [ABSTRACT FROM AUTHOR]

In independent validation cohorts, the level of EVP concentration was higher in CRLM patients compared to BD patients ( I p i = 0.04; Fig. Quantification of EVPs from human serum samples A Boxplot analysis of EVP concentration in the discovery cohort. Kaplan-Meier curves of the validation cohorts indicated that the EVP signature could distinguish between the risk of recurrence and death of CRLM patients (internal: p < 0.0001 and external: I p i = 0.004 validation cohort; Fig. [Extracted from the article]

Background: We evaluated the potential of diffusion-weighted MRI (DW-MRI) and 18F-FDG-PET for the early prediction of a triple negative breast cancer (TNBC) response to cisplatin.Methods: Cisplatin-treated TNBC tumor-bearing mice were categorized as responders or non-responders based on the tumor growth rate. DW-MRI and 18F-FDG-PET were performed before and after treatment (day 0 and days 3 and 7, respectively). The average apparent diffusion coefficient value (ADCmean), the highest standardized uptake value (SUVmax), and the metabolic tumor volume (MTV) were measured. The ratios of each parameter relative to day 0 were calculated [ΔADCmean (day 3) and (day 7), ΔSUVmax (day 3) and (day 7), and ΔMTV (day 3) and (day 7), respectively]. Overall survival rates were compared based on the thresholds determined by these parameters.Results: Both the day 3 and day 7 ratios of ADCmean and MTV showed significant differences between the responder and non-responder groups, whereas the ratios of SUVmax did not. Mice with ΔADCmean (day 3) exceeding the threshold showed a longer overall survival rate. Mice with ΔSUVmax (day 7), ΔMTV (day 3), and ΔMTV (day 7) below the respective thresholds showed a longer overall survival rate.Conclusions: The ratios of ADCmean, SUVmax, and MTV have the potential to predict the therapeutic response and to screen non-responders in the ultra-early phase following cisplatin treatment in patients with TNBC. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) has infected about 200 million individuals across the world and is known as the major cause of liver disease . Objectives: Viral load measurement at early stages of the therapy in Hepatitis C patients is believed to be a more effective tool to predict the sustained virological response (SVR). The primary aim of the present study was to evaluate whether the decline in viral load of HCV at early stages of the therapy may predict the treatment response. Another objective was to see the benefits of therapy extension in non-responders. Study Design: Descriptive, analytical study. Setting: Shalamar Hospital Lahore. Period: November 2010 to October 2013. Methods: Four hundred and thirty patients, chronically infected with different genotypes of Hepatitis C virus were treated with Interferon alpha 2b plus Ribavirin (IFNα-2b + RBV). Viral load was assessed at day zero, week four, in the mid time of therapy and at the end of therapy. The treatment duration was extended 12-24 weeks (according to HCV genotypes) in non-responders. Results: The patients with <2 MIU/ mL viral load at day zero, able to drop ⩾2 log viral load at week-4 or showed no virus at the time of half therapy completion, exhibited better response. The extension of therapy was more beneficial for those non-responder who had <0.05 MIU/mL viral load at the end point of therapy than those who had ⩾0.05 MIU/mL at that stage. Conclusions: The viral load detection at early stages of the therapy will be useful in clinical practice. Moreover, the patients with <0.05 MIU/ mL viral load at the end of therapy are suitable candidates for the therapy extension. [ABSTRACT FROM AUTHOR]

18 F-FDG PET/CT is potentially applicable to predict response to chemotherapy in combination with bevacizumab in patients with advanced non-small cell lung cancer (NSCLC). Methods: In 25 patients with advanced nonsquamous NSCLC, 18 F-FDG PET/CT was performed before treatment and after 2 wk, at the end of the second week of first cycle carboplatin-paclitaxel and bevacizumab (CPB) treatment. Patients received up to a total of 4 cycles of CPB treatment. Maintenance treatment with bevacizumab monotherapy was continued until progressive disease without significant treatment-related toxicities of first-line treatment. In the case of progressive disease, bevacizumab was combined with erlotinib. SUV corrected for lean body mass (SUL and SUL peak ) were obtained. PERCIST were used for response evaluation. These semiquantitative parameters were correlated with progression-free survival and overall survival (OS). Results: Metabolic response, defined by a significant reduction in SUL peak of 30% or more after 2 wk of CPB, was predictive of progression-free survival and OS. For partial metabolic responders ( n = 19), the median OS was 22.8 mo. One-year and 2-y OS were 79% and 47%, respectively. Nonmetabolic responders ( n = 6) (stable metabolic disease or progressive disease) showed a median OS of 4.4 mo (1-y and 2-y OS was 33% and 0%, respectively) ( P < 0.001). Conclusion: 18 F-FDG PET/CT after 1 treatment cycle is predictive of outcome to first-line chemotherapy with bevacizumab in patients with advanced nonsquamous NSCLC. This enables identification of patients at risk of treatment failure, permitting treatment alternatives such as early switch to a different therapy., (© 2017 by the Society of Nuclear Medicine and Molecular Imaging.)

Unlabelled: The potential of (18)F-FDG PET changes was evaluated for prediction of response to concomitant chemoradiotherapy in patients with locally advanced non-small cell lung cancer (NSCLC)., Methods: For 28 patients, (18)F-FDG PET was performed before treatment, at the end of the second week of treatment, and at 2 wk and 3 mo after the completion of treatment. Standardized uptake value (SUV), maximum SUV, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained. Early metabolic changes were defined as fractional change (ΔTLG) when (18)F-FDG PET at the end of the second week was compared with pretreatment (18)F-FDG PET. In-treatment metabolic changes, as measured by serial (18)F-FDG PET, were correlated with standard criteria of response evaluation of solid tumors by means of CT imaging (Response Evaluation Criteria In Solid Tumors 1.1). Parameters were analyzed for stratification in progression-free survival (PFS)., Results: When compared with early metabolic nonresponders, a ΔTLG decrease of 38% or more was associated with a significantly longer PFS (1-y PFS 80% vs. 36%, P = 0.02). Pretreatment TLG was found to be a prognostic factor for PFS., Conclusion: The degree of change in TLG was predictive for response to concomitant chemoradiotherapy as early as the end of the second week into treatment for patients with locally advanced NSCLC. Pretreatment TLG was prognostic for PFS.