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Correlation of RECIST, Computed Tomography Morphological Response, and Pathological Regression in Hepatic Metastasis Secondary to Colorectal Cancer: The AVAMET Study.

ABSTRACT: Background: The prospective phase IV AVAMET study was undertaken to correlate response evaluation criteria in solid tumors (RECIST)-defined response rates with computed tomography-based morphological criteria (CTMC) and pathological response after liver resection of colorectal cancer metastases. Methods: Eligible patients were aged ?18 years, with Eastern Cooperative Oncology Group (ECOG) performance status 0/1 and histologically-confirmed colon or rectal adenocarcinoma with measurable liver metastases. Preoperative treatment was bevacizumab (7.5 mg on day 1) + XELOX (oxaliplatin 130 mg/m2, capecitabine 1000 mg/m2 bid on days 1-14 q3w). After three cycles, response was evaluated by a multidisciplinary team. Patients who were progression-free and metastasectomy candidates received one cycle of XELOX before undergoing surgery 3-5 weeks later, followed by four cycles of bevacizumab + XELOX. Results: A total of 83 patients entered the study; 68 were eligible for RECIST, 67 for CTMC, and 51 for pathological response evaluation. Of these patients, 49% had a complete or partial RECIST response, 91% had an optimal or incomplete CTMC response, and 81% had a complete or major pathological response. CTMC response predicted 37 of 41 pathological responses versus 23 of 41 responses predicted using RECIST (p = 0.008). Kappa coefficients indicated a lack of correlation between the results of RECIST and morphological responses and between morphological and pathological response rates. Conclusion: CTMC may represent a better marker of pathological response to bevacizumab + XELOX than RECIST in patients with potentially-resectable CRC liver metastases.

SUBMITTER: Vera R

PROVIDER: S-EPMC7465835 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Correlation of RECIST, Computed Tomography Morphological Response, and Pathological Regression in Hepatic Metastasis Secondary to Colorectal Cancer: The AVAMET Study.

Vera Ruth R Gómez María Luisa ML Ayuso Juan Ramón JR Figueras Joan J García-Alfonso Pilar P Martínez Virginia V Lacasta Adelaida A Ruiz-Casado Ana A Safont María José MJ Aparicio Jorge J Campos Juan Manuel JM Cámara Juan Carlos JC Martín-Richard Marta M Montagut Clara C Pericay Carles C Vieitez Jose María JM Falcó Esther E Jorge Mónica M Marín Miguel M Salgado Mercedes M Viúdez Antonio A

Cancers 20200812 8

<i>Background</i>: The prospective phase IV AVAMET study was undertaken to correlate response evaluation criteria in solid tumors (RECIST)-defined response rates with computed tomography-based morphological criteria (CTMC) and pathological response after liver resection of colorectal cancer metastases. <i>Methods</i>: Eligible patients were aged ≥18 years, with Eastern Cooperative Oncology Group (ECOG) performance status 0/1 and histologically-confirmed colon or rectal adenocarcinoma with measur ...[more]

PMID: 32806731

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Project description:Purpose: To investigate the correlation between 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters and clinicopathological factors in pathological subtypes of invasive lung adenocarcinoma and prognosis. Patients and Methods: Metabolic parameters and clinicopathological factors from 176 consecutive patients with invasive lung adenocarcinoma between August 2008 and August 2016 who underwent 18F-FDG PET/CT examination were retrospectively analyzed. Invasive lung adenocarcinoma was divided into five pathological subtypes:lepidic predominant adenocarcinoma (LPA), acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), solid predominant adenocarcinoma (SPA), and micropapillary predominant adenocarcinoma (MPA). The differences in metabolic parameters [maximal standard uptake value (SUVmax), mean standard uptake value (SUVmean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV)] and tumor diameter for different pathological subtypes were analyzed. Patients were divided into two groups according to their prognosis: good prognosis group (LPA, APA, PPA) and poor prognosis group (SPA, MPA). Logistic regression was used to filter predictors and construct a predictive model, and areas under the receiver operating curve (AUC) were calculated. Cox regression analysis was performed on prognostic factors. Results: 82 (46.6%) females and 94 (53.4%) males of patients with invasive lung adenocarcinoma were enrolled in this study. Metabolic parameters and tumor diameter of different pathological subtype had statistically significant (P < 0.05). The predictive model constructed using independent predictors (Distant metastasis, Ki-67, and SUVmax) had good classification performance for both groups. The AUC for SUVmax was 0.694 and combined with clinicopathological factors were 0.745. Cox regression analysis revealed that Stage, TTF-1, MTV, and pathological subtype were independent risk factors for patient prognosis. The hazard ratio (HR) of the poor prognosis group was 1.948 (95% CI 1.042-3.641) times the good prognosis group. The mean survival times of good and poor prognosis group were 50.2621 (95% CI 47.818-52.706) and 35.8214 (95% CI 27.483-44.159) months, respectively, while the median survival time was 47.00 (95% CI 45.000-50.000) and 31.50 (95% CI 23.000-49.000) months, respectively. Conclusion: PET/CT metabolic parameters combined with clinicopathological factors had good classification performance for the different pathological subtypes, which may provide a reference for treatment strategies and prognosis evaluation of patients.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.MethodsPatients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.ResultsA total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765-0.9585) and 0.8088 (95% CI: 0.7064-0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353-0.8987) and 0.8017 (95% CI: 0.6878-0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646-0.9824) and an AUC of 0.9099 (95% CI: 0.8324-0.9873) for the training cohort and validation cohort, respectively.ConclusionWe developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Project description:BackgroundWalnuts are grown worldwide in temperate areas and producers are facing an increasing demand. In a climate change context, the industry also needs cultivars that provide fruits of quality. This quality includes satisfactory filling ratio, thicker shell, ease of cracking, smooth shell and round-shaped walnut, and larger nut size. These desirable traits have been analysed so far using calipers or micrometers, but it takes a lot of time and requires the destruction of the sample. A challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts, that are used to characterize new cultivars or collections in any germplasm management process.ResultsIn this study, we develop a method to measure different morphological traits on several walnuts simultaneously such as morphometric traits (nut length, nut face and profile diameters), traits that previously required opening the nut (shell thickness, kernel volume and filling kernel/nut ratio) and traits that previously were difficult to quantify (shell rugosity, nut sphericity, nut surface area and nut shape). These measurements were obtained from reconstructed 3D images acquired by X-ray computed tomography (CT). A workflow was created including several steps: noise elimination, walnut individualization, properties extraction and quantification of the different parts of the fruit. This method was applied to characterize 50 walnuts of a part of the INRAE walnut germplasm collection made of 161 unique accessions, obtained from the 2018 harvest. Our results indicate that 50 walnuts are sufficient to phenotype the fruit quality of one accession using X-ray CT and to find correlations between the morphometric traits. Our imaging workflow is suitable for any walnut size or shape and provides new and more accurate measurements.ConclusionsThe fast and accurate measurement of quantitative traits is of utmost importance to conduct quantitative genetic analyses or cultivar characterization. Our imaging workflow is well adapted for accurate phenotypic characterization of a various range of traits and could be easily applied to other important nut crops.

Project description:BackgroundSensitivity to neoadjuvant chemotherapy in locally advanced gastric cancer patients varies; however, an effective predictive marker is currently lacking. We aimed to propose and validate a practical treatment efficacy prediction method based on contrast-enhanced computed tomography (CECT) radiomics.MethodData of l24 locally advanced gastric carcinoma patients who underwent neoadjuvant chemotherapy were acquired retrospectively between December 2012 and August 2020 from three different cancer centers. In total, 1216 radiomics features were initially extracted from each lesion's pretreatment portal venous phase computed tomography image. Subsequently, a radiomics predictive model was constructed using machine learning software. Clinicopathological data and radiological parameters of the enrolled patients were collected and analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to screen for independent predictive indices. Finally, we developed an integrated model combining clinicopathological predictive parameters and radiomics features.ResultIn the training set, 10 (14.9%) patients achieved a good response (GR) after preoperative neoadjuvant chemotherapy (n = 77), whereas in the testing set, seven (17.5%) patients achieved a GR (n = 47). The radiomics predictive model showed competitive prediction efficacy in both the training and independent external validation sets. The areas under the curve (AUC) values were 0.827 (95% confidence interval [CI]: 0.609-1.000) and 0.854 (95% CI: 0.610-1.000), respectively. Similarly, when only the single hospital data were included as an independent external validation set (testing set 2), AUC values of the models were 0.827 (95% CI: 0.650-0.952) and 0.889 (95% CI: 0.663-1.000) in the training set and testing set 2, respectively.ConclusionOur study is the first to discover that CECT radiomics could provide powerful and consistent predictions of therapeutic sensitivity to neoadjuvant chemotherapy among gastric cancer patients across different hospitals.

Project description:Background and purposeThe Alberta Stroke Program Early Computed Tomography (CT) Score (ASPECTS) and CT perfusion (CTP) are commonly used to predict the ischemic core in acute ischemic strokes. CT angiography source images (CTA-SI) can also provide additional information to identify the extent of ischemia. Our objective was to investigate the correlation of noncontrast CT (NCCT) ASPECTS and CTA-SI ASPECTS with CTP core volumes.MethodsWe utilized a single institutional, retrospective registry of consecutive patients with acute ischemic stroke with large vessel occlusion between May 2016 and May 2018. We graded ASPECTS both on baseline NCCT and CTA-SI and measured CTP core using automated RAPID software (cerebral blood flow <30%). We used Spearman's correlation coefficients to evaluate the correlation between continuous variables.ResultsA total of 52 patients fit the inclusion criteria of large vessel occlusion in 6 to 24 hours and baseline imaging work up of NCCT, CTA, and CTP. The median age was 63 (interquartile range=53.5-75) and 38.46% were female. The median NCCT ASPECTS was 7 (interquartile range=6-9), CTA-SI ASPECTS was 5 (interquartile range=4-7), and CTP core was 14.5 mL (interquartile range=0-46 mL). There was a moderate correlation between NCCT ASPECTS and CTP core (rs=-0.55, P<0.0001) and between CTA-SI ASPECTS and CTP core (rs=-0.50, P=0.0002). The optimal NCCT ASPECTS cutoff score to detect CTP core ≤70 mL was ≥6 (sensitivity, 0.84; specificity, 0.57; positive predictive value, 0.93; negative predictive value, 0.36) and the optimal CTA-SI ASPECTS was ≥5 (sensitivity, 0.76; specificity, 0.71; positive predictive value, 0.94; negative predictive value, 0.31).ConclusionsThere was a moderate correlation between NCCT and CTA-SI ASPECTS in predicting CTP defined ischemic core in delayed time windows. Further studies are needed to determine if NCCT and CTA imaging could be used for image-based patient selection when CTP imaging is not available.

Dataset Information

Correlation of RECIST, Computed Tomography Morphological Response, and Pathological Regression in Hepatic Metastasis Secondary to Colorectal Cancer: The AVAMET Study.

Publications

Correlation of RECIST, Computed Tomography Morphological Response, and Pathological Regression in Hepatic Metastasis Secondary to Colorectal Cancer: The AVAMET Study.

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