Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer

2021 ◽  
pp. 20210191
Author(s):  
Liuhui Zhang ◽  
Donggen Jiang ◽  
Chujie Chen ◽  
Xiangwei Yang ◽  
Hanqi Lei ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Multiparametric Mri ◽  
Curve Analysis ◽  
Training Set ◽  
Threshold Probability ◽  
Roc Curve Analysis ◽  
Aggressive Prostate Cancer ◽  
Therapeutic Decisions ◽  
Validation Set ◽  
Radiomics Signature

Objectives: To develop and validate a noninvasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy. Methods: In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T2WI (n = 788) and DWI (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic (ROC) curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature. Results: nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%. Conclusions: The multiparametric MRI-based radiomics signature can potentially serve as a noninvasive tool for distinguishing between indolent and aggressive PCa prior to therapy. Advances in knowledge: The multiparametric MRI-based radiomics signature has the potential to noninvasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.

scholarly journals Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Wei Ma ◽  
Fangkun Zhao ◽  
Xinmiao Yu ◽  
Shu Guan ◽  
Huandan Suo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Curve Analysis ◽  
Time Dependent ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. Methods We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. Results A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p = 1.215e − 06 in the training set; p = 0.0069 in the validation set; p = 1.233e − 07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR = 1.432; 95% CI 1.204–1.702, p < 0.001), validation set (HR = 1.162; 95% CI 1.004–1.345, p = 0.044), and whole set (HR = 1.240; 95% CI 1.128–1.362, p < 0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

scholarly journals Immune-Related LncRNAs as Predictors of Survival in Breast Cancer: A Prognostic Signature

2020 ◽  
Author(s):  
wei ma ◽  
fangkun zhao ◽  
xinmiao yu ◽  
shu guan ◽  
huandan suo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Curve Analysis ◽  
Time Dependent ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancers development and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separatedinto training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate and multivariate Cox regression analyses. Results: A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group( p= 1.215e−06 in the training set; p =0.0069 in the validation set; p =1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p <0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p <0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions: We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

scholarly journals Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

2020 ◽  
Author(s):  
wei ma ◽  
fangkun zhao ◽  
xinmiao yu ◽  
shu guan ◽  
huandan suo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Curve Analysis ◽  
Time Dependent ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer.Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses.Results:A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group(p=1.215e−06 in the training set; p=0.0069 in the validation set; p=1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set,0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p<0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p<0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways.Conclusions:We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

scholarly journals Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Siye Liu ◽  
Xiaoping Yu ◽  
Songhua Yang ◽  
Pingsheng Hu ◽  
Yingbin Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Cancer ◽  
Clinical Features ◽  
Primary Tumor ◽  
Venous Invasion ◽  
Curve Analysis ◽  
Imaging Data ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Radiomics Signature

ObjectiveTo establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.MethodsThe clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves.ResultsThe radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801.ConclusionThe radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.

scholarly journals Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Zongtai Zheng ◽  
Feijia Xu ◽  
Zhuoran Gu ◽  
Yang Yan ◽  
Tianyuan Xu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Multiparametric Mri ◽  
Data System ◽  
Invasive Bladder Cancer ◽  
Training Set ◽  
Muscle Invasive Bladder Cancer ◽  
Invasive Status ◽  
Validation Set ◽  
Muscle Invasive ◽  
Radiomics Signature

BackgroundThe treatment and prognosis for muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) are different. We aimed to construct a nomogram based on the multiparametric MRI (mpMRI) radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score for the preoperative differentiation of MIBC from NMIBC.MethodThe retrospective study involved 185 pathologically confirmed bladder cancer (BCa) patients (training set: 129 patients, validation set: 56 patients) who received mpMRI before surgery between August 2014 to April 2020. A total of 2,436 radiomics features were quantitatively extracted from the largest lesion located on the axial T2WI and from dynamic contrast-enhancement images. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature screening. The selected features were introduced to construct radiomics signatures using three classifiers, including least absolute shrinkage and selection operator (LASSO), support vector machines (SVM) and random forest (RF) in the training set. The differentiation performances of the three classifiers were evaluated using the area under the curve (AUC) and accuracy. Univariable and multivariable logistic regression were used to develop a nomogram based on the optimal radiomics signature and clinical characteristics. The performance of the radiomics signatures and the nomogram was assessed and validated in the validation set.ResultsCompared to the RF and SVM classifiers, the LASSO classifier had the best capacity for muscle invasive status differentiation in both the training (accuracy: 90.7%, AUC: 0.934) and validation sets (accuracy: 87.5%, AUC: 0.906). Incorporating the radiomics signature and VI-RADS score, the nomogram demonstrated better discrimination and calibration both in the training set (accuracy: 93.0%, AUC: 0.970) and validation set (accuracy: 89.3%, AUC: 0.943). Decision curve analysis showed the clinical usefulness of the nomogram.ConclusionsThe mpMRI radiomics signature may be useful for the preoperative differentiation of muscle-invasive status in BCa. The proposed nomogram integrating the radiomics signature with the VI-RADS score may further increase the differentiation power and improve clinical decision making.

scholarly journals Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ji Hyung Nam ◽  
Youngbae Hwang ◽  
Dong Jun Oh ◽  
Junseok Park ◽  
Ki Bae Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Capsule Endoscopy ◽  
Curve Analysis ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Standard Criterion ◽  
Validation Set ◽  
Calculation Software ◽  
Clinical Grading

AbstractA standardized small bowel (SB) cleansing scale is currently not available. The aim of this study was to develop an automated calculation software for SB cleansing score using deep learning. Consecutively performed capsule endoscopy cases were enrolled from three hospitals. A 5-step scoring system based on mucosal visibility was trained for deep learning in the training set. Performance of the trained software was evaluated in the validation set. Average cleansing score (1.0 to 5.0) by deep learning was compared to clinical grading (A to C) reviewed by clinicians. Cleansing scores decreased as clinical grading worsened (scores of 4.1, 3.5, and 2.9 for grades A, B, and C, respectively, P < 0.001). Adequate preparation was achieved for 91.7% of validation cases. The average cleansing score was significantly different between adequate and inadequate group (4.0 vs. 2.9, P < 0.001). ROC curve analysis revealed that a cut-off value of cleansing score at 3.25 had an AUC of 0.977. Diagnostic yields for small, hard-to-find lesions were associated with high cleansing scores (4.3 vs. 3.8, P < 0.001). We developed a novel scoring software which calculates objective, automated cleansing scores for SB preparation. The cut-off value we suggested provides a standard criterion for adequate bowel preparation as a quality indicator.

scholarly journals Is There a Role for Prostate Tumour Overexpressed-1 in the Diagnosis of HGPIN and of Prostatic Adenocarcinoma? A Comparison with α-Methylacyl CoA Racemase

2012 ◽  
Vol 25 (1) ◽  
pp. 67-74 ◽  
Author(s):  
M. Scarpelli ◽  
R. Mazzucchelli ◽  
F. Barbisan ◽  
A. Santinelli ◽  
A. Lopez-Beltran ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Accuracy ◽  
Differential Display ◽  
Roc Curve ◽  
Intraepithelial Neoplasia ◽  
Prostatic Intraepithelial Neoplasia ◽  
Curve Analysis ◽  
High Grade ◽  
Roc Curve Analysis ◽  
Prostate Tumour

Prostate Tumour Overexpressed-1 (PTOV1) was recently identified as a novel gene and protein during a differential display screening for genes overexpressed in prostate cancer (PCa). α-Methyl-CoA racemose (AMACR) mRNA was identified as being overexpressed in PCa. PTOV1 and racemase were immunohistochemically evaluated in PCa, high-grade prostatic intraepithelial neoplasia (HGPIN), atrophy and normal-looking epithelium (NEp) in 20 radical prostatectomies (RPs) with pT2a Gleason score 6 prostate cancer with the aim of analyzing the differences in marker expression between PTOV1 and AMACR. The level of expression of PTOV1 and AMACR increased from NEp and atrophy through HGPIN, away from and adjacent to prostate cancer, to PCa. With the ROC curve analysis the overall accuracy in distinguishing PCa vs HGPIN away from and adjacent to cancer was higher for AMACR than for PTOV1. In conclusion, AMACR can be considered a more accurate marker than PTOV1 in the identification of HGPIN and of PCa. However, PTOV1 may aid in the diagnosis of PCa, at least to supplement AMACR as another positive marker of carcinoma and to potentially increase diagnostic accuracy.

Use of percent positive biopsy cores to predict prostate cancer–specific death in patients treated with dose-escalated radiotherapy.

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 35-35
Author(s):  
Y. Qian ◽  
F. Y. Feng ◽  
S. Halverson ◽  
K. Blas ◽  
H. M. Sandler ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Roc Curve ◽  
Risk Group ◽  
Curve Analysis ◽  
External Beam Radiation ◽  
Rank Test ◽  
Roc Curve Analysis ◽  
Cut Point ◽  
Log Rank Test ◽  
Positive Biopsy

35 Background: The percent of positive biopsy cores (PPC)-considered a surrogate of local disease burden-has been shown to predict biochemical failure (BF) after external beam radiation therapy (EBRT), but most series have used conventional dose RT. Dose-escalated RT has been demonstrated to improve prostate cancer outcomes, but the value of PPC is unclear in the setting of RT doses high enough to decrease local failure. Methods: A retrospective evaluation was performed of 651 patients treated to ≥75 Gy with biopsy core information available. Patients were stratified for PPC by quartile, and differences by quartile in BF, freedom from metastasis (FFM), cause specific survival (CSS), and overall survival (OS) were assessed using the log-rank test. Receiver operated characteristic (ROC) curve analysis was utilized to determine an optimal cut-point for PPC. Cox proportional hazards multivariate regression was utilized to assess the impact of PPC on clinical outcome when adjusting for risk group. Results: With median follow-up of 62 months the median number of cores sampled was 7 (IQR: 6–12) with median PPC in 38% (IQR: 17%-67%). On log-rank test, BF, FFM, and CSS were all associated with PPC (p < 0.005 for all), with worse outcomes only for the highest PPC quartile (>67%). There was no observed difference in OS based upon PPC. ROC curve analysis confirmed a cut-point of 67% as most closely associated with CSS (p<0.001, AUC=0.71). On multivariate analysis after adjusting for NCCN risk group and ADT use, PPC>67% increased the risk for BF (p<0.0001, HR:2.1 [1.4–3.0]), FFM (p<0.05, HR:1.7 [1.1 to 2.9]), and CSS (p<0.06 (HR:2.1 [1.0–4.6]). When analyzed as a continuous variable controlling for risk group and ADT use, increasing PPC increased the risk for BF (p < 0.002), metastasis (p < 0.05), and CSS (p < 0.02), with a 1–2% increase in relative risk of recurrence for each 1% increase in the PPC. Conclusions: For patients treated with dose-escalated RT, the PPC adds prognostic value but at a higher cut-point then previously utilized. Patients with PPC >67% remain at increased risk for failure even with dose-escalated EBRT and may receive benefit from further intensification of therapy. No significant financial relationships to disclose.

Screening for aggressive prostate cancer: A single-center experience using the 4Kscore and multiparametric MRI for the detection of Gleason 7 or higher prostate cancer.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 84-84
Author(s):  
Vivek Venkatramani ◽  
Bruno Nahar ◽  
Tulay Koru-Sengul ◽  
Nachiketh Soodana-Prakash ◽  
Mark L. Gonzalgo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Significant Proportion ◽  
Curve Analysis ◽  
Risk Scores ◽  
Consecutive Series ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Aggressive Prostate Cancer ◽  
Prostate Cancer Detection

84 Background: While non-invasive biomarkers and multi-parametric MRI (mpMRI) are routinely used for prostate cancer detection, few studies have assessed their performance together. We evaluated the performance of mpMRI and the 4Kscore for the detection of significant prostate cancer. Methods: We identified a consecutive series of men who underwent an mpMRI and 4Kscore for evaluation of prostate cancer at the University of Miami. We selected those who underwent a biopsy of the prostate. The primary outcome was the presence of Gleason 7 or higher cancer on biopsy. The 4Kscore was modeled as a continuous variable, but also categorized into low ( < 7.5%), intermediate (7.5-20%), and high ( > 20) risk scores. The mpMRI was categorized as being either negative or positive for a suspicion of cancer. We used logistic regression and Decision Curve Analysis to report the discrimination and clinical utility of using mpMRI and the 4Kscore for prostate cancer detection. Finally, we modeled the probability of harboring a Gleason 7 or higher prostate cancer based on various categories of the 4Kscore and mpMRI. Results: Among 235 men who underwent a 4Kscore and mpMRI, only 112 (52%) were referred for a biopsy, allowing a significant proportion of men to avoid a biopsy. Among those who had a biopsy, the AUC for using the 4Kscore and mpMRI together [0.81(0.72-0.90)] was superior to using the 4Kscore [0.71(0.61-0.81);p = 0.004] and mpMRI [0.74(0.65-0.83);p = 0.02] alone. Similarly, decision curve analysis revealed the highest net benefit for using both tests together, compared to either test alone. Finally, we found that having a positive mpMRI was a predictor of aggressive cancer in the higher two 4Kscores, but not in the lowest category, suggesting that men with a low 4Kscore may not benefit from getting an mpMRI, most likely due to the low likelihood of cancer and having a positive mpMRI. Conclusions: The 4Kscore and mpMRI provides independent, but complementary, information to enhance the prediction of aggressive prostate cancer. Prospective trials are required to confirm these findings.

Training and validation study for sequential monitoring of CAMLs in circulation to predict ongoing progression in lung cancer patients undergoing definitive radiotherapy.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3053-3053
Author(s):  
Daniel Adams ◽  
Jianzhong He ◽  
Yawei Qiao ◽  
Ting Xu ◽  
Hui Gao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Radiation Therapy ◽  
Cancer Patients ◽  
Stage Iv ◽  
Progression Free Survival ◽  
Training Set ◽  
Lung Cancer Patients ◽  
Therapeutic Decisions ◽  
Validation Set ◽  
Nsclc Patients

3053 Background: Cancer Associated Macrophage-Like cells (CAMLs) are a recently described circulating stromal cell common in the peripheral blood of cancer patients that are prognostic for progressive disease. Further, it has been shown that changes in CAML size (i.e. enlargement above 50µm) can predict progression free survival (PFS) in thoracic cancers (e.g. lung). We enrolled 104 unresectable non-small cell lung cancer (NSCLC) patients, with an initial training set review of 54 patients, to determine if change in CAML size after radiation therapy was predictive PFS. Methods: A 2 year single blind prospective study was undertaken to test the relationship of ≥50µm CAMLs to PFS based on imaging in lung patients before and after induction of chemo radiation, or radiation therapy. To achieve a 2-tailed 90% power (α = 0.05) we recruited a training set of 54 patients and validation set of 50 patients all with pathologically confirmed unresectable NSCLC: Stage I (n = 14), Stage II (n = 16), Stage III (n = 61) & Stage IV (n = 13). Baseline (BL) blood samples were taken prior to start of therapy & a 2nd blood sample (T1) was taken after completion of radiotherapy (~30 days). Blood was filtered by CellSieve filtration and CAMLs quantified. Analysis by CAML size of < 49 µm or ≥50 µm was used to evaluate PFS hazard ratios (HRs) by censored univariate & multivariate analysis. Results: CAMLs were found in 95% of samples averaging 2.7 CAMLs/7.5mL sample at BL, with CAMLs ≥50 µm having reduced PFS (HR = 2.2, 95%CI1.3-3.8, p = 0.003). At T1, 18 patients had increased CAML size ≥50 µm with PFS (HR = 4.6, 95%CI2.5-8.3, p < 0.001). In total, ≥50 µm CAMLs at BL was 76% accurate at predicting progression within 24 months while ≥50 µm CAMLs at T1 was 83% accurate at predicting progression. Conclusions: In unresectable NSCLC patients, enlargement of CAMLs during treatment is an indicator active progression. We identify that a single ≥50 µm CAML after induction of radiotherapy, in our training set and confirmed in our validation set, is an indicator of poor prognosis. We suggest that changes in CAML size during therapy may indicate the efficacy of therapy and could potentially help shape subsequent therapeutic decisions.

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