Prognostic Impact of Intratumoral Heterogeneity Based on Fractal Geometry Analysis in Operated NSCLC Patients

Castello, Angelo; Russo, Carlo; Grizzi, Fabio; Qehajaj, Dorina; Lopci, Egesta

doi:10.1007/s11307-018-1299-3

Cited by 7 publications

(6 citation statements)

References 34 publications

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“…Measures, such as image intensity, contrast, and texture, allow for clear delineation of internal structures, 99m Tc, 111 In and 123 I) and PET (e.g., 11 C and 18 F)], which can be integrated into any organic metabolite. 18 F is used to label glucose analog, 18 [153][154][155][156][157][158][159][160][161][162] a Abbreviations: CA, contrast agents; CNR, contrast-to-noise ratio; CT, computer tomography; DCE, dynamic contrast enhanced; DW, diffusion weighted; MRI, magnetic resonance imaging; PET, positron emission tomography; SNR, signal-to-noise ratio; SPECT, single photon emission computer tomography.…”

Section: Measuring Spatial Molecular and Microenvironmental Heterogen...mentioning

confidence: 99%

“…The same is true for tumor ploidy (i.e., the average cancer genome copy number) [100,101]. A second issue of Esophageal cancer [141] Colorectal cancer [142] Pancreatic cancer [143] Gastric cancer [144] Lung and head-and-neck cancer [150] Clear cell renal cell carcinoma [152] PET Cervix and head and neck cancer [157] Esophageal carcinoma [158] Lung cancer [159] MRI Breast cancer [164][165][166] Non-Hodgkin lymphoma [167] Glioblastoma [168] Fractal dimension CT Gastric cancer [144] Colorectal cancer [146] Gastrointestinal stromal cancer [147] Hepatocellular carcinoma [148] PET Non-small cell lung carcinoma [160] MRI Glioblastoma [170] Limb sarcoma [171] Colorectal cancer liver metastases [172] Wavelet coefficients CT Lung and head-and-neck cancer [150] MRI Cervical cancer [173] Breast cancer [174] PET Esophageal carcinoma [158] Table 4. Mathematical equations needed for multiple heterogeneity metrics…”

Section: Immune Heterogeneitymentioning

confidence: 99%

“…In lung cancer, metabolic tumor heterogeneity was also quantified using texture features [159]. In non-small cell lung carcinoma [160], 3D-fractal dimension extracted from 18 FDG-PET/CT images was found to be an independent prognostic factor of disease-free survival, and combining glucose metabolism using SUV with morphological complexity described by fractal dimension improved diagnostic accuracy [161]. Importantly, 18 FDG-PET features, including texture heterogeneity metrics, were found to be associated with histopathological features computed on H&E images, providing a link between radiomic and phenotypic ITH [162].…”

Section: Radiomic Heterogeneitymentioning

confidence: 99%

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Quantification of tumor heterogeneity: from data acquisition to metric generation

Kashyap

Rapsomaniki

Barros

et al. 2022

Trends in Biotechnology

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Section: Measuring Spatial Molecular and Microenvironmental Heterogen...mentioning

confidence: 99%

Section: Immune Heterogeneitymentioning

confidence: 99%

Section: Radiomic Heterogeneitymentioning

confidence: 99%

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Quantification of tumor heterogeneity: from data acquisition to metric generation

Kashyap

Rapsomaniki

Barros

et al. 2022

Trends in Biotechnology

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“…Meanwhile, some studies have reported that tumor heterogeneity is associated with treatment response and patient prognosis. [4][5][6] Therefore, a stage-specific prediction model should be further developed to accurately confirm the histological subtype of NSCLC prior to treatment.…”

Section: Introductionmentioning

confidence: 99%

Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

Ji¹,

Qiu²,

et al. 2021

CMAR

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Purpose: To investigate the impact of staging on differences in glucose metabolic heterogeneity between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) by 18 F-fluorodeoxyglucose positron emission tomography (18 F-FDG PET) textural analysis and to develop a stage-specific PET radiomic prediction model to distinguish lung ADC from SCC. Patients and Methods: Patients who were histologically diagnosed with lung ADC or SCC and underwent pretreatment 18 F-FDG PET/CT scans were retrospectively identified. Radiomic features were extracted from a semiautomatically outlined tumor region in the Chang-Gung Image Texture Analysis (CGITA) software package. The differences in radiomic parameters between lung ADC and SCC were compared stage-by-stage in 253 consecutive NSCLC patients with stages I to III disease. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. A radiomic signature for each stage was subsequently constructed and evaluated. Then, an individual nomogram incorporating the radiomic signature and clinical risk factors was established and evaluated. The performance of the constructed models was assessed by receiver operating characteristic (ROC) curve analysis, and the nomogram was further validated by calibration curve analysis. Results: The performance of the radiomic signature for distinguishing lung ADC and SCC in both the training and validation cohorts was good, with AUCs of 0.883, 0.854, and 0.895 in the training cohort and 0.932, 0.944, and 0.886 in the validation cohort for stages I, II, and III NSCLC, respectively. The radiomic-clinical nomogram integrating radiomic features with independent clinical predictors exhibited more favorable discriminative performance, with AUCs of 0.982, 0.963, and 0.979 in the training cohort and 0.989, 0.984, and 0.978 in the validation cohort for stages I, II, and III, respectively. Conclusion: Differences in PET radiomic features between lung ADC and SCC varied in different stages. Stage-specific PET radiomic prediction models provided more favorable performance for discriminating the histological subtype of NSCLC.

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“…Although tumor analytics are claimed to be refined and sensitized down to a single cell level, thus enabling facilitated tumor detection and characterization in the peripheral blood [ 22 ], the clinical relevance of this approach analyzing a huge number of cells is unclear. Furthermore, the detection of a single tumor cell type does not necessarily reflect the tumor, with its inherent heterogeneity, since NSCLC tumor heterogeneity has been known and characterized in various studies for a long time [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. However, such considerations have rarely been addressed in therapeutically oriented studies.…”

Section: Introductionmentioning

confidence: 99%

Influence of Biopsy Technique on Molecular Genetic Tumor Characterization in Non-Small Cell Lung Cancer—The Prospective, Randomized, Single-Blinded, Multicenter PROFILER Study Protocol

et al. 2020

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The detection of molecular alterations is crucial for the individualized treatment of advanced non-small cell lung cancer (NSCLC). Missing targetable alterations may have a major impact on patient’s progression free and overall survival. Although laboratory testing for molecular alterations has continued to improve; little is known about how biopsy technique affects the detection rate of different mutations. In the retrospective study detection rate of epidermal growth factor (EGFR) mutations in tissue extracted by bronchoscopic cryobiopsy (CB was significantly higher compared to other standard biopsy techniques. This prospective, randomized, multicenter, single blinded study evaluates the accuracy of molecular genetic characterization of NSCLC for different cell sampling techniques. Key inclusion criteria are suspected lung cancer or the suspected relapse of known NSCLC that is bronchoscopically visible. Patients will be randomized, either to have a CB or a bronchoscopic forceps biopsy (FB). If indicated, a transbronchial needle aspiration (TBNA) of suspect lymph nodes will be performed. Blood liquid biopsy will be taken before tissue biopsy. The primary endpoint is the detection rate of molecular genetic alterations in NSCLC, using CB and FB. Secondary endpoints are differences in the combined detection of molecular genetic alterations between FB and CB, TBNA and liquid biopsy. This trial plans to recruit 540 patients, with 178 evaluable patients per study cohort. A histopathological and molecular genetic evaluation will be performed by the affiliated pathology departments of the national network for genomic medicine in lung cancer (nNGM), Germany. We will compare the diagnostic value of solid tumor tissue, lymph node cells and liquid biopsy for the molecular genetic characterization of NSCLC. This reflects a real world clinical setting, with potential direct impact on both treatment and survival.

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Prognostic Impact of Intratumoral Heterogeneity Based on Fractal Geometry Analysis in Operated NSCLC Patients

Cited by 7 publications

References 34 publications

Quantification of tumor heterogeneity: from data acquisition to metric generation

Quantification of tumor heterogeneity: from data acquisition to metric generation

Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

Influence of Biopsy Technique on Molecular Genetic Tumor Characterization in Non-Small Cell Lung Cancer—The Prospective, Randomized, Single-Blinded, Multicenter PROFILER Study Protocol

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