scholarly journals CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer

2020 ◽  
Author(s):  
Kai-Yu Sun ◽  
Hang-Tong Hu ◽  
Shu-Ling Chen ◽  
Jin-Ning Ye ◽  
Guang-Hua Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Validation Cohort ◽  
Clinical Score ◽  
Feature Reduction ◽  
Portal Venous Phase ◽  
Clinical Variables ◽  
Significant Difference ◽  
Response To Neoadjuvant Chemotherapy

Abstract Background: Neoadjuvant chemotherapy is a promising treatment option for potential resectable gastric cancer, but patients’ responses vary. We aimed to develop and validate a radiomics score (rad_score) to predict treatment response to neoadjuvant chemotherapy and to investigate its efficacy in survival stratification.Methods: A total of 106 patients with neoadjuvant chemotherapy before gastrectomy were included (training cohort: n=74; validation cohort: n=32). Radiomics features were extracted from the pre-treatment portal venous-phase CT. After feature reduction, a rad_score was established by Randomised Tree algorithm. A rad_clinical_score was constructed by integrating the rad_score with clinical variables, so was a clinical score by clinical variables only. The three scores were validated regarding their discrimination and clinical usefulness. The patients were stratified into two groups according to the score thresholds (updated with post-operative clinical variables), and their survivals were compared. Results: In the validation cohort, the rad_score demonstrated a good predicting performance in treatment response to the neoadjuvant chemotherapy (AUC [95% CI] =0.82 [0.67, 0.98]), which was better than the clinical score (based on pre-operative clinical variables) without significant difference (0.62 [0.42, 0.83], P=0.09). The rad_clinical_score could not further improve the performance of the rad_score (0.70 [0.51, 0.88], P=0.16). Based on the thresholds of these scores, the high-score groups all achieved better survivals than the low-score groups in the whole cohort (all P<0.001).Conclusion: The rad_score that we developed was effective in predicting treatment response to neoadjuvant chemotherapy and in stratifying patients with gastric cancer into different survival groups. Our proposed strategy is useful for individualised treatment planning.

scholarly journals CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer

2020 ◽  
Author(s):  
Kai-Yu Sun ◽  
Hang-Tong Hu ◽  
Shu-Ling Chen ◽  
Jin-Ning Ye ◽  
Guang-Hua Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Validation Cohort ◽  
Clinical Score ◽  
Feature Reduction ◽  
Portal Venous Phase ◽  
Clinical Variables ◽  
Significant Difference ◽  
Response To Neoadjuvant Chemotherapy

Abstract Background: Neoadjuvant chemotherapy is a promising treatment option for potential resectable gastric cancer, but patients’ responses vary. We aimed to develop and validate a radiomics score (rad_score) to predict treatment response to neoadjuvant chemotherapy and to investigate its efficacy in survival stratification.Methods: A total of 106 patients with neoadjuvant chemotherapy before gastrectomy were included (training cohort: n=74; validation cohort: n=32). Radiomics features were extracted from the pre-treatment portal venous-phase CT. After feature reduction, a rad_score was established by Randomised Tree algorithm. A rad_clinical_score was constructed by integrating the rad_score with clinical variables, so was a clinical score by clinical variables only. The three scores were validated regarding their discrimination and clinical usefulness. The patients were stratified into two groups according to the score thresholds (updated with post-operative clinical variables), and their survivals were compared. Results: In the validation cohort, the rad_score demonstrated a good predicting performance in treatment response to the neoadjuvant chemotherapy (AUC [95% CI] =0.82 [0.67, 0.98]), which was better than the clinical score (based on pre-operative clinical variables) without significant difference (0.62 [0.42, 0.83], P=0.09). The rad_clinical_score could not further improve the performance of the rad_score (0.70 [0.51, 0.88], P=0.16). Based on the thresholds of these scores, the high-score groups all achieved better survivals than the low-score groups in the whole cohort (all P<0.001).Conclusion: The rad_score that we developed was effective in predicting treatment response to neoadjuvant chemotherapy and in stratifying patients with gastric cancer into different survival groups. Our proposed strategy is useful for individualised treatment planning.

Development and Validation of CT-based Radiomics Scores for Prediction of Response to Neoadjuvant Chemotherapy and Survival in Gastric Cancer

2019 ◽  
Author(s):  
Kai-Yu Sun ◽  
Hang-Tong Hu ◽  
Shu-Ling Chen ◽  
Jin-Ning Ye ◽  
Guang-Hua Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Validation Cohort ◽  
Clinical Score ◽  
Feature Reduction ◽  
Portal Venous Phase ◽  
Clinical Variables ◽  
Predicting Performance ◽  
Significant Difference

Abstract Background: Neoadjuvant chemotherapy is a promising treatment option for potential resectable gastric cancer, but patients’ responses varied. We aimed to develop and validate a radiomics score (rad_score) to predict the treatment response of neoadjuvant chemotherapy, and to investigate its efficacy in survival stratification. Methods: A total of 106 patients with neoadjuvant chemotherapy before gastrectomy were included (training cohort: n=74; validation cohort: n=32). Radiomics features were extracted from the pre-treatment portal venous-phase CT. After feature reduction, a rad_score was established by Randomized Tree algorithm. A rad_clinical_score was constructed by integrating the rad_score with clinical variables, so was a clinical score by clinical variables only. The three scores were validated regarding their discrimination and clinical usefulness. According to the score thresholds (updated with post-operative clinical variables), patients were stratified into two groups and their survivals were compared.Results: In the validation cohort, the rad_score demonstrated a good predicting performance in treatment response of neoadjuvant chemotherapy (AUC [95% CI] =0.82 [0.67, 0.98]), which was better than the clinical score (based on pre-operative clinical variables) without significant difference (0.62 [0.42, 0.83], P=0.09). The rad_clinical_score could not further improve the performance of rad_score (0.70 [0.51, 0.88], P=0.16). Based on the thresholds of these scores, the high-score groups all achieved better survivals than the low-score groups in the whole cohort (all P<0.001). Conclusion: The rad_score was effective in predicting treatment response of neoadjuvant chemotherapy and stratifying patients’ survival for gastric cancer, which assisted in individualized treatment planning.

scholarly journals A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yonghe Chen ◽  
Kaikai Wei ◽  
Dan Liu ◽  
Jun Xiang ◽  
Gang Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Advanced Gastric Cancer ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Pathological Response ◽  
Validation Data ◽  
Response To Neoadjuvant Chemotherapy

AimsTo develop and validate a model for predicting major pathological response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on a machine learning algorithm.MethodA total of 221 patients who underwent NAC and radical gastrectomy between February 2013 and September 2020 were enrolled in this study. A total of 144 patients were assigned to the training cohort for model building, and 77 patients were assigned to the validation cohort. A major pathological response was defined as primary tumor regressing to ypT0 or T1. Radiomic features extracted from venous-phase computed tomography (CT) images were selected by machine learning algorithms to calculate a radscore. Together with other clinical variables selected by univariate analysis, the radscores were included in a binary logistic regression analysis to construct an integrated prediction model. The data obtained for the validation cohort were used to test the predictive accuracy of the model.ResultA total of 27.6% (61/221) patients achieved a major pathological response. Five features of 572 radiomic features were selected to calculate the radscores. The final established model incorporates adenocarcinoma differentiation and radscores. The model showed satisfactory predictive accuracy with a C-index of 0.763 and good fitting between the validation data and the model in the calibration curve.ConclusionA prediction model incorporating adenocarcinoma differentiation and radscores was developed and validated. The model helps stratify patients according to their potential sensitivity to NAC and could serve as an individualized treatment strategy-making tool for AGC patients.

scholarly journals P-282 Factors predicting response to neoadjuvant chemotherapy in gastric cancer

2021 ◽  
Vol 32 ◽  
pp. S193
Author(s):  
P. Kumar CG ◽  
D. Muduly ◽  
M. Imaduddin ◽  
S. Ambre ◽  
L. Colney ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Response To Neoadjuvant Chemotherapy

scholarly journals Relationship Between Obesity and Pathologic Response to Neoadjuvant Chemotherapy Among Women With Operable Breast Cancer

2008 ◽  
Vol 26 (25) ◽  
pp. 4072-4077 ◽  
Author(s):  
Jennifer K. Litton ◽  
Ana M. Gonzalez-Angulo ◽  
Carla L. Warneke ◽  
Aman U. Buzdar ◽  
Shu-Wan Kau ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Pathologic Complete Response ◽  
Operable Breast Cancer ◽  
Obese Patients ◽  
Breast Cancer Patients ◽  
Significant Difference ◽  
Response To Neoadjuvant Chemotherapy

Purpose To understand the mechanism through which obesity in breast cancer patients is associated with poorer outcome, we evaluated body mass index (BMI) and response to neoadjuvant chemotherapy (NC) in women with operable breast cancer. Patients and Methods From May 1990 to July 2004, 1,169 patients were diagnosed with invasive breast cancer at M. D. Anderson Cancer Center and received NC before surgery. Patients were categorized as obese (BMI ≥ 30 kg/m2), overweight (BMI of 25 to < 30 kg/m2), or normal/underweight (BMI < 25 kg/m2). Logistic regression was used to examine associations between BMI and pathologic complete response (pCR). Breast cancer–specific, progression-free, and overall survival times were examined using the Kaplan-Meier method and Cox proportional hazards regression analysis. All statistical tests were two-sided. Results Median age was 50 years; 30% of patients were obese, 32% were overweight, and 38% were normal or underweight. In multivariate analysis, there was no significant difference in pCR for obese compared with normal weight patients (odds ratio [OR] = 0.78; 95% CI, 0.49 to 1.26). Overweight and the combination of overweight and obese patients were significantly less likely to have a pCR (OR = 0.59; 95% CI, 0.37 to 0.95; and OR = 0.67; 95% CI, 0.45 to 0.99, respectively). Obese patients were more likely to have hormone-negative tumors (P < .01), stage III tumors (P < .01), and worse overall survival (P = .006) at a median follow-up time of 4.1 years. Conclusion Higher BMI was associated with worse pCR to NC. In addition, its association with worse overall survival suggests that greater attention should be focused on this risk factor to optimize the care of breast cancer patients.

scholarly journals Early assessment of shear wave elastography parameters foresees the response to neoadjuvant chemotherapy in patients with invasive breast cancer

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Juanjuan Gu ◽  
Eric C. Polley ◽  
Max Denis ◽  
Jodi M. Carter ◽  
Sandhya Pruthi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Shear Wave ◽  
Invasive Breast Cancer ◽  
Shear Wave Elastography ◽  
Early Prediction ◽  
Early Assessment ◽  
Significant Difference ◽  
Mass Size ◽  
Response To Neoadjuvant Chemotherapy

Abstract Background Early prediction of tumor response to neoadjuvant chemotherapy (NACT) is crucial for optimal treatment and improved outcome in breast cancer patients. The purpose of this study is to investigate the role of shear wave elastography (SWE) for early assessment of response to NACT in patients with invasive breast cancer. Methods In a prospective study, 62 patients with biopsy-proven invasive breast cancer were enrolled. Three SWE studies were conducted on each patient: before, at mid-course, and after NACT but before surgery. A new parameter, mass characteristic frequency (fmass), along with SWE measurements and mass size was obtained from each SWE study visit. The clinical biomarkers were acquired from the pre-NACT core-needle biopsy. The efficacy of different models, generated with the leave-one-out cross-validation, in predicting response to NACT was shown by the area under the receiver operating characteristic curve and the corresponding sensitivity and specificity. Results A significant difference was found for SWE parameters measured before, at mid-course, and after NACT between the responders and non-responders. The combination of Emean2 and mass size (s2) gave an AUC of 0.75 (0.95 CI 0.62–0.88). For the ER+ tumors, the combination of Emean_ratio1, s1, and Ki-67 index gave an improved AUC of 0.84 (0.95 CI 0.65–0.96). For responders, fmass was significantly higher during the third visit. Conclusions Our study findings highlight the value of SWE estimation in the mid-course of NACT for the early prediction of treatment response. For ER+ tumors, the addition of Ki-67improves the predictive power of SWE. Moreover, fmass is presented as a new marker in predicting the endpoint of NACT in responders.

Response to neoadjuvant chemotherapy and impact on survival for resected gastric cancer.

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 122-122
Author(s):  
Erin Greenleaf ◽  
Christopher S Hollenbeak ◽  
Joyce Wong
Keyword(s):  
Gastric Cancer ◽  
Adjuvant Chemotherapy ◽  
Neoadjuvant Chemotherapy ◽  
Disease Progression ◽  
Median Time ◽  
Pathologic Response ◽  
Early Stage Disease ◽  
Stage Disease ◽  
The Impact ◽  
Response To Neoadjuvant Chemotherapy

122 Background: This study assesses the survival impact of perioperative chemotherapy, with further analysis of pathologic response to neoadjuvant chemotherapy (NAC), in patients undergoing gastrectomy for gastric cancer (GC) in a large US sample. Methods: Using the 2003-2012 ACS National Cancer Database, 16,128 patients underwent gastrectomy for cancer. Treatment groups were categorized as: NAC, adjuvant chemotherapy, and surgery only. Patients receiving NAC were further categorized as: down-staged, no response, and disease progression. Univariate and multivariate analyses were performed to estimate the impact of treatment on overall survival. Results: Of patients undergoing gastrectomy, 36.6% received NAC, 19.5% received adjuvant chemotherapy, and 43.9% underwent surgery only. Median time of survival was longer in patients with more advanced disease who underwent either NAC or adjuvant chemotherapy versus surgery alone (see Table). In multivariate analysis, patients who received NAC had 20% lower hazard of death than surgery only patients (HR = 0.80, p < 0.0001). Within the NAC cohort (N = 5,909), 47.7% were down-staged, 36.5% had no response, and 15.7% demonstrated disease progression. Having a pathologic response to NAC was associated with having private insurance (OR = 1.22, p < 0.0001), higher socioeconomic status (OR = 1.21, p = 0.003), treatment in the central US (p < 0.0001, both), and undergoing proximal gastrectomy (OR = 1.59, p < 0.0001). Among patients who received NAC, median time of survival was longer if NAC down-staged patients to stages 0 or 1, with no survival difference in advanced stage disease. Conclusions: Neoadjuvant chemotherapy elicits a survival benefit in patients with advanced GC. Pathologic response is achieved in nearly half of patients undergoing NAC and is associated with improved survival, although only when down-staging to early stage disease. [Table: see text] [Table: see text]

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