scholarly journals Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients

2021 ◽  
Author(s):  
Stefano Bracci ◽  
Miriam Dolciami ◽  
Claudio Trobiani ◽  
Antonella Izzo ◽  
Angelina Pernazza ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Advanced Nsclc ◽  
Validation Cohort ◽  
Locally Advanced ◽  
Area Under The Curve ◽  
Gray Level ◽  
Quantitative Ct ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Ct Texture Analysis

Abstract Purpose The assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC. Methods By applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort. Results The Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of − 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively. Conclusion Rad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.

Distinguishing fibrosis/necrosis from teratoma or viable disease in the retroperitoneum in post-chemotherapy, nonseminomatous testicular germ cell tumor using quantitative CT texture analysis.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 563-563
Author(s):  
Kevin George King ◽  
Sumeet Bhanvadia ◽  
Saum Ghodoussipour ◽  
Darryl Hwang ◽  
Bino Varghese ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Germ Cell ◽  
Texture Analysis ◽  
Testicular Germ Cell Tumor ◽  
Cell Tumor ◽  
Gray Level ◽  
Square Root ◽  
Quantitative Ct ◽  
Testicular Germ Cell ◽  
Ct Texture Analysis

563 Background: In metastatic nonseminomatous testicular germ cell tumor (NSGCT), post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) is indicated for residual masses > 1 cm because of these 45% will be fibrosis/necrosis, 45% will be teratoma and 15% will be viable malignancy. There is no imaging test that reliably distinguishes lymph nodes (LNs) with tumor (teratoma or malignancy) from LNs with fibrosis/necrosis. We evaluated whether quantitative CT texture analysis (TA) could make this differentiation. Methods: Pre- and post-chemotherapy CTs (all same phase and slice thickness) were reviewed in 22 NSGCT patients with RP LNs > 1 cm post chemotherapy. After manual segmentation of RP LNs on a 3D workstation, 187 TA metrics were derived, using 2D/3D gray-level co-occurrence matrix (GLCM), 2D/3D gray-level difference matrix (GLDM), and spectral analysis. Metrics were derived 2 ways: from post-chemotherapy CTs alone, and also as a difference between pre- and post-chemotherapy CTs, resulting in 374 metrics. PC-RPLND pathology was correlated with CT data at 88 LN stations in these 22 patients. Results: 15 imaging metrics showed a significant difference (p ≤ 0.05) between LN stations with only fibrosis/necrosis and those with teratoma or viable tumor. Seven were derived from the difference between pre- and post-chemotherapy CTs: 4 using a 2D GLCM (coronal standard deviation, coronal square root of variance, coronal mean, and coronal sum of average), and 3 using a 2D GLDM (axial variance, axial square root of variance, and coronal variance). The other 8 were derived from post-chemotherapy CTs alone: 7 using a 2D GLCM (sagittal square root of variance, sagittal standard deviation, coronal square root of variance, coronal mean, coronal standard deviation, coronal sum of average, and coronal entropy) and 1 using a 2D GLDM (sagittal sum entropy). Conclusions: CT TA shows promise in differentiating necrosis from teratoma or viable tumor in RP LNs in post-chemotherapy NSGCT. A larger study is needed to further test this method, towards a long-term goal of potentially allowing some patients to avoid PC-RPLND.

Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab

Gut ◽  
2019 ◽  
Vol 69 (3) ◽  
pp. 531-539 ◽  
Author(s):  
Anthony Dohan ◽  
Benoit Gallix ◽  
Boris Guiu ◽  
Karine Le Malicot ◽  
Caroline Reinhold ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Texture Analysis ◽  
Validation Cohort ◽  
External Validation ◽  
Hepatic Metastases ◽  
Survival Prediction ◽  
Training Cohort ◽  
Link Type ◽  
Contrast Enhanced Ct ◽  
Morphological Criteria

PurposeThe objective of this study was to build and validate a radiomic signature to predict early a poor outcome using baseline and 2-month evaluation CT and to compare it to the RECIST1·1 and morphological criteria defined by changes in homogeneity and borders.MethodsThis study is an ancillary study from the PRODIGE-9 multicentre prospective study for which 491 patients with metastatic colorectal cancer (mCRC) treated by 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) and bevacizumab had been analysed. In 230 patients, computed texture analysis was performed on the dominant liver lesion (DLL) at baseline and 2 months after chemotherapy. RECIST1·1 evaluation was performed at 6 months. A radiomic signature (Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced CT TextuRe Analysis (SPECTRA) Score) combining the significant predictive features was built using multivariable Cox analysis in 120 patients, then locked, and validated in 110 patients. Overall survival (OS) was estimated with the Kaplan-Meier method and compared between groups with the logrank test. An external validation was performed in another cohort of 40 patients from the PRODIGE 20 Trial.ResultsIn the training cohort, the significant predictive features for OS were: decrease in sum of the target liver lesions (STL), (adjusted hasard-ratio(aHR)=13·7, p=1·93×10–7), decrease in kurtosis (ssf=4) (aHR=1·08, p=0·001) and high baseline density of DLL, (aHR=0·98, p<0·001). Patients with a SPECTRA Score >0·02 had a lower OS in the training cohort (p<0·0001), in the validation cohort (p<0·0008) and in the external validation cohort (p=0·0027). SPECTRA Score at 2 months had the same prognostic value as RECIST at 6 months, while non-response according to RECIST1·1 at 2 months was not associated with a lower OS in the validation cohort (p=0·238). Morphological response was not associated with OS (p=0·41).ConclusionA radiomic signature (combining decrease in STL, density and computed texture analysis of the DLL) at baseline and 2-month CT was able to predict OS, and identify good responders better than RECIST1.1 criteria in patients with mCRC treated by FOLFIRI and bevacizumab as a first-line treatment. This tool should now be validated by further prospective studies.Trial registrationClinicaltrial.gov identifier of the PRODIGE 9 study: NCT00952029.Clinicaltrial.gov identifier of the PRODIGE 20 study: NCT01900717.

scholarly journals Real-world prospective analysis of treatment patterns in durvalumab maintenance after chemoradiotherapy in unresectable, locally advanced NSCLC patients

2021 ◽  
Author(s):  
Julian Taugner ◽  
Lukas Käsmann ◽  
Chukwuka Eze ◽  
Alexander Rühle ◽  
Amanda Tufman ◽  
...  
Keyword(s):  
Prospective Study ◽  
Real World ◽  
Maintenance Treatment ◽  
Progressive Disease ◽  
Advanced Nsclc ◽  
Locally Advanced ◽  
Contrast Enhanced Ct ◽  
Unresectable Stage ◽  
Locally Advanced Nsclc

SummaryThe aim of this prospective study is to evaluate the clinical use and real-world efficacy of durvalumab maintenance treatment after chemoradiotherapy (CRT) in unresectable stage, locally advanced non-small cell lung cancer (NSCLC). All consecutive patients with unresectable, locally advanced NSCLC and PD-L1 expression (≥1%) treated after October 2018 were included. Regular follow up, including physical examination, PET/CT and/or contrast-enhanced CT-Thorax/Abdomen were performed every three months after CRT. Descriptive treatment pattern analyses, including reasons of discontinuation and salvage treatment, were undertaken. Statistics were calculated from the last day of thoracic irradiation (TRT). Twenty-six patients were included. Median follow up achieved 20.6 months (range: 1.9–30.6). Durvalumab was initiated after a median of 25 (range: 13–103) days after completion of CRT. In median 14 (range: 2–24) cycles of durvalumab were applied within 6.4 (range 1–12.7) months. Six patients (23%) are still in treatment and seven (27%) have completed treatment with 24 cycles. Maintenance treatment was discontinued in 13 (50%) patients: 4 (15%) patients developed grade 3 pneumonitis according to CTCAE v5 after a median of 3.9 (range: 0.5–11.6) months and 7 (range: 2–17) cycles of durvalumab. Four (15%) patients developed grade 2 skin toxicity. One (4%) patient has discontinued treatment due to incompliance. Six and 12- month progression-free survival (PFS) rates were 82% and 62%, median PFS was not reached. No case of hyperprogression was documented. Eight (31%) patients have relapsed during maintenance treatment after a median of 4.8 (range: 2.2–11.3) months and 11 (range: 6–17) durvalumab cycles. Two patients (9%) developed a local-regional recurrence after 14 and 17 cycles of durvalumab. Extracranial distant metastases and brain metastases as first site of failure were detected in 4 (15%) and 2 (8%) patients, respectively. Three (13%) patients presented with symptomatic relapse. Our prospective study confirmed a favourable safety profile of durvalumab maintenance treatment after completion of CRT in unresectable stage, locally advanced NSCLC in a real-world setting. In a median follow-up time of 20.6 months, durvalumab was discontinued in 27% of all patients due to progressive disease. All patients with progressive disease were eligible for second-line treatment.

scholarly journals Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
Textural Features ◽  
Mutation Status ◽  
Portal Venous Phase ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Venous Phase ◽  
Ct Texture Analysis

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Radiology ◽  
2015 ◽  
Vol 276 (3) ◽  
pp. 787-796 ◽  
Author(s):  
Taryn Hodgdon ◽  
Matthew D. F. McInnes ◽  
Nicola Schieda ◽  
Trevor A. Flood ◽  
Leslie Lamb ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Ct Images ◽  
Renal Angiomyolipoma ◽  
Quantitative Ct ◽  
Unenhanced Ct ◽  
Ct Texture Analysis

scholarly journals Quantitative CT texture analysis for diagnosing systemic sclerosis

Medicine ◽  
2019 ◽  
Vol 98 (29) ◽  
pp. e16423 ◽  
Author(s):  
Gianluca Milanese ◽  
Manoj Mannil ◽  
Katharina Martini ◽  
Britta Maurer ◽  
Hatem Alkadhi ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Texture Analysis ◽  
Quantitative Ct ◽  
Ct Texture Analysis

scholarly journals A Novel Online Calculator Based on Serum Biomarkers to Detect Hepatocellular Carcinoma among Patients with Hepatitis B

2019 ◽  
Vol 65 (12) ◽  
pp. 1543-1553 ◽  
Author(s):  
Tian Yang ◽  
Hao Xing ◽  
Guoqiang Wang ◽  
Nianyue Wang ◽  
Miaoxia Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B ◽  
Validation Cohort ◽  
Area Under The Curve ◽  
Serum Biomarkers ◽  
Web Based ◽  
Training Cohort ◽  
B Virus ◽  
Online Calculator ◽  
Diagnostic Sensitivity And Specificity

Abstract BACKGROUND Early detection of hepatocellular carcinoma (HCC) among hepatitis B virus (HBV)-infected patients remains a challenge, especially in China. We sought to create an online calculator of serum biomarkers to detect HCC among patients with chronic hepatitis B (CHB). METHODS Participants with HBV-HCC, CHB, HBV-related liver cirrhosis (HBV-LC), benign hepatic tumors, and healthy controls (HCs) were recruited at 11 Chinese hospitals. Potential serum HCC biomarkers, protein induced by vitamin K absence or antagonist-II (PIVKA-II), α-fetoprotein (AFP), lens culinaris agglutinin A-reactive fraction of AFP (AFP-L3) and α-L-fucosidase (AFU) were evaluated in the pilot cohort. The calculator was built in the training cohort via logistic regression model and validated in the validation cohort. RESULTS In the pilot study, PIVKA-II and AFP showed better diagnostic sensitivity and specificity compared with AFP-L3 and AFU and were chosen for further study. A combination of PIVKA-II and AFP demonstrated better diagnostic accuracy in differentiating patients with HBV-HCC from patients with CHB or HBV-LC than AFP or PIVKA-II alone [area under the curve (AUC), 0.922 (95% CI, 0.908–0.935), sensitivity 88.3% and specificity 85.1% for the training cohort; 0.902 (95% CI, 0.875–0.929), 87.8%, and 81.0%, respectively, for the validation cohort]. The nomogram including AFP, PIVKA-II, age, and sex performed well in predicting HBV-HCC with good calibration and discrimination [AUC, 0.941 (95% CI, 0.929–0.952)] and was validated in the validation cohort [AUC, 0.931 (95% CI, 0.909–0.953)]. CONCLUSIONS Our results demonstrated that a web-based calculator including age, sex, AFP, and PIVKA-II accurately predicted the presence of HCC in patients with CHB. ClinicalTrials.gov Identifier NCT03047603

Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatectomy or liver transplantation.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4079-4079
Author(s):  
Hidetoshi Nitta ◽  
Marc Antoine Allard ◽  
Mylene Sebagh ◽  
Gabriella Pittau ◽  
Oriana Ciacio ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Predictive Model ◽  
Validation Cohort ◽  
Microvascular Invasion ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

4079 Background: Microvascular invasion (MVI) is the strongest prognostic factor following surgery of hepatocellular carcinoma (HCC). However, it is usually not available on the preoperative setting. A predictive model of MVI in patients scheduled for hepatic resection (HR) or liver transplantation (LT) would thus help guiding treatment strategy. The aim of this study was to develop a predictive model for MVI of HCC before either HR or LT. Methods: HCC patients who consecutively performed HR or LT from January 1994 to June 2016 at a single institution were subdivided into a training and validation cohort. Risk factors for MVI in the training cohort were used to develop a predictive model for MVI, to be validated in the validation cohort. The outcomes of the HR and LT patients with high or low MVI probability based on the model, were compared using propensity score matching (PSM). Cut-off values for continuous factors were determined based on ROC curve analysis. Results: A total of 910 patients (425 HR, 485 LT) were included in the training (n = 637) and validation (n = 273) cohorts. In the training cohort, multivariate analysis demonstrated that alpha-fetoprotein ≥100ng/ml ( p < 0.0001), largest tumor size ≥40mm ( p = 0.0002), non-boundary HCC type on contrast-enhanced CT ( p = 0.001), neutrophils-to-lymphocytes ratio ≥3.2 ( p = 0.002), aspartate aminotransferase ≥62U/l ( p = 0.02) were independently associated with MVI. Combinations of these 5 factors varied the MVI probability from 15.5% to 91.1%. This predictive model achieved a good c-index of 0.76 in the validation cohort. In PSM (109 HR, 109 LT), there was no difference in survival between HR and LT patients among the high MVI probability (≥50%) patients, (5y-OS; 46.3% vs 42.2%, p = 0.77, 5y-RFS; 54.0% vs 28.8%, p = 0.21). Among the low probability ( < 50%), survival was significantly decreased following HR compared with LT (5y-OS; 54.1% vs 78.8%, p = 0.007, 5y-RFS; 17.3% vs 86.1%, p< 0.0001). Conclusions: This model developed from preoperative data allows reliable prediction of MVI, and may thus help with preoperative decisions about the suitability of HR or LT in patients with HCC.

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