Radiomics Features Differentiate Between Normal and Tumoral High-Fdg Uptake

Hsu, Chih‐Yang; Doubrovin, Mikhail; Hua, Chia‐Ho; Mohammed, Omar J.; Shulkin, Barry L.; Kaste, Sue C.; Federico, Sara M.; Metzger, M; Krasin, Matthew J.; Tinkle, Christopher L.; Merchant, Thomas E.; Lucas, John T.

doi:10.1038/s41598-018-22319-4

Cited by 20 publications

(11 citation statements)

References 45 publications

(31 reference statements)

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“…A previous study showed that statistical RMS was able to predict pathological response after chemoradiation in non-small cell lung cancer (NSCLC), by identifying gross residual response 35 . Statistical RMS combined with other intensity statistical features was able to distinguish bladder tumor tissue from other normal tissues in fluorodeoxyglucose-positron emission tomography (FDG-PET) scan 36 .…”

Section: Discussionmentioning

confidence: 99%

Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening

Pérez‐Morales

Tunali

Stringfield

et al. 2020

Sci Rep

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the national Lung Screening trial (nLSt) demonstrated that screening with low-dose computed tomography (LDCT) is associated with a 20% reduction in lung cancer mortality. One potential limitation of LDct screening is overdiagnosis of slow growing and indolent cancers. in this study, peritumoral and intratumoral radiomics was used to identify a vulnerable subset of lung patients associated with poor survival outcomes. incident lung cancer patients from the nLSt were split into training and test cohorts and an external cohort of non-screen detected adenocarcinomas was used for further validation. After removing redundant and non-reproducible radiomics features, backward elimination analyses identified a single model which was subjected to Classification and Regression tree to stratify patients into three risk-groups based on two radiomics features (nGtDM Busyness and Statistical Root Mean Square [RMS]). The final model was validated in the test cohort and the cohort of non-screen detected adenocarcinomas. Using a radio-genomics dataset, Statistical RMS was significantly associated with FOXF2 gene by both correlation and two-group analyses. our rigorous approach generated a novel radiomics model that identified a vulnerable high-risk group of early stage patients associated with poor outcomes. these patients may require aggressive follow-up and/or adjuvant therapy to mitigate their poor outcomes. The National Lung Screening Trial (NLST) demonstrated that annual screening with low-dose helical computed tomography (LDCT) compared to chest radiography is associated with a 20% relative reduction in lung cancer mortality among high-risk individuals 1. However, LDCT screening can lead to overdiagnosis and overtreatment of slow growing, indolent cancers that may pose no threat if left untreated 2,3. Prior post-hoc analyses of the NLST have estimated that 18-22.5% of screen-detected cancers would not become symptomatic in a patient's lifetime and would remain as indolent lung cancer 4. At present there is limited data regarding the potential harmful impact of overdiagnosis on lung cancer outcomes; however, studies have suggested overdiagnosis is associated with increased operative mortality, severe disability among survivors, and reduction in longer term disease-free survival attributed to loss of pulmonary reserve 5. Though clinical guidelines provide recommendations for the management of screen-detected nodules, there are limited tools to discriminate between indolent and aggressive lung cancers diagnosed in the lung cancer screening setting 6-9. As such, biomarkers that can classify behavior of screen-detected lung cancers is an unmet clinical need since prior studies have suggested that 10 to 27% of lung cancers are over-diagnosed in lung cancer screening 10-13. Quantitative image features, also known as radiomics 14 , are non-invasive biomarkers that are generated from medical imaging and reflect the underlying tumor pathophysiology and heterogeneity. Radiomics have many advantages over circulating and tissue-based bio...

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Section: Discussionmentioning

confidence: 99%

Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening

Pérez‐Morales

Tunali

Stringfield

et al. 2020

Sci Rep

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“…The study indicated that PET/MR was more efficient in characterization and visualization and showed high lesion detection and good image quality of NPC compared with PET/CT. Some studies have shown that the combination of PET and MRI images and the comprehensive analysis of the molecular metabolism and microstructure characteristics of the tumors are of great value in the differential diagnosis and prognosis analysis of tumors (5,6). However, there are relatively few PET/MR studies on the staging of NPC.…”

Section: Introductionmentioning

confidence: 99%

Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging

Feng

Liang²,

Wang

et al. 2020

Front. Oncol.

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Objective: Accurate staging is of great importance in treatment selection for patients with nasopharyngeal carcinoma (NPC). The aims of this study were to construct radiomic models of NPC staging based on positron emission tomography (PET) and magnetic resonance (MR) images and to investigate the correlation between metabolic parameters and radiomic features. Methods: A total of 100 consecutive cases of NPC (70 in training and 30 in the testing cohort) with undifferentiated carcinoma confirmed pathologically were recruited. Metabolic parameters of the local lesions of NPC were measured. A total of 396 radiomic features based on PET and MRI images were calculated [including histogram, Haralick, shape factor, gray level co-occurrence matrix (GLCM), and run length matrix (RLM)] and selected [using maximum relevance and minimum redundancy (mRMR) and least shrinkage and selection operator (LASSO)], respectively. The logistic regression models were established according to these features. Finally, the relationship between the metabolic parameters and radiomic features was analyzed. Results: We selected the nine most relevant radiomic features (six from MR images and three from PET images) from local NPC lesions. In the PET model, the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and the specificity of the training group were 0.84, 0.75, 0.90, and 0.69, respectively. In the MR model, those metrics were 0.85, 0.83, 0.75, and 0.86, respectively. Pearson's correlation analysis showed that the metabolic parameters had different degrees of correlation with the selected radiomic features. Conclusion: The PET and MR radiomic models were helpful in the diagnosis of NPC staging. There were correlations between the metabolic parameters and radiomic features of primary NPC based on PET/MR. In the future, PET/MR-based radiomic Feng et al. NPC Radiomics Based on PET/MR Imaging models, with further improvement and validation, can be a more useful and economical tool for predicting local invasion and distant metastasis of NPC.

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“…The automatic heart classification is challenging [ 23 ] due to the variable uptake behavior. In half of our patients with misclassified heart volumes an untypical cardiac uptake pattern was seen.…”

Section: Discussionmentioning

confidence: 99%

“…This sensitive threshold [ 24 , 25 ] was chosen to include all potential tumor lesions in our evaluation. Different segmentation thresholds were used by other authors: fixed thresholds of an SUV of 2.5 [ 12 , 16 ], 3.0 [ 23 ] or 4.0 [ 26 ], or adaptive thresholds of 41%/42% of the SUVmax [ 11 , 16 , 26 , 27 ] or background-related thresholds [ 12 , 16 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

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Automatic classification of lymphoma lesions in FDG-PET–Differentiation between tumor and non-tumor uptake

et al. 2022

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Introduction The automatic classification of lymphoma lesions in PET is a main topic of ongoing research. An automatic algorithm would enable the swift evaluation of PET parameters, like texture and heterogeneity markers, concerning their prognostic value for patients outcome in large datasets. Moreover, the determination of the metabolic tumor volume would be facilitated. The aim of our study was the development and evaluation of an automatic algorithm for segmentation and classification of lymphoma lesions in PET. Methods Pre-treatment PET scans from 60 Hodgkin lymphoma patients from the EuroNet-PHL-C1 trial were evaluated. A watershed algorithm was used for segmentation. For standardization of the scan length, an automatic cropping algorithm was developed. All segmented volumes were manually classified into one of 14 categories. The random forest method and a nested cross-validation was used for automatic classification and evaluation. Results Overall, 853 volumes were segmented and classified. 203/246 tumor lesions and 554/607 non-tumor volumes were classified correctly by the automatic algorithm, corresponding to a sensitivity, a specificity, a positive and a negative predictive value of 83%, 91%, 79% and 93%. In 44/60 (73%) patients, all tumor lesions were correctly classified. In ten out of the 16 patients with misclassified tumor lesions, only one false-negative tumor lesion occurred. The automatic classification of focal gastrointestinal uptake, brown fat tissue and composed volumes consisting of more than one tissue was challenging. Conclusion Our algorithm, trained on a small number of patients and on PET information only, showed a good performance and is suitable for automatic lymphoma classification.

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Radiomics Features Differentiate Between Normal and Tumoral High-Fdg Uptake

Cited by 20 publications

References 45 publications

Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening

Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening

Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging

Automatic classification of lymphoma lesions in FDG-PET–Differentiation between tumor and non-tumor uptake

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