Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

Texture analysis can predict response to etoposide-doxorubicin-cisplatin in patients with adrenocortical carcinoma

BACKGROUND

The adrenocortical carcinoma (ACC) is a rare and highly aggressive malignancy originating from the adrenal cortex. These patients usually undergo chemotherapy with etoposide, doxorubicin, cisplatin and mitotane (EDP-M) in case of locally advanced or metastatic ACC. Computed tomography (CT) radiomics showed to be useful in adrenal pathologies. The study aimed to analyze the association between response to EDP-M treatment and CT textural features at diagnosis in patients with locally advanced or metastatic ACCs.

METHODS

We enrolled 17 patients with advanced or metastatic ACC who underwent CT before and after EDP-M therapy. The response to treatment was evaluated according to RECIST 1.1, Choi, and volumetric criteria. Based on the aforementioned criteria, the patients were classified as responders and not responders. Textural features were extracted from the biggest lesion in contrast-enhanced CT images with LifeX software. ROC curves were drawn for the variables that were significantly different (p < 0.05) between the two groups.

RESULTS

Long-run high grey level emphasis (LRHGLE_GLRLM) and histogram kurtosis were significantly different between responder and not responder groups (p = 0.04) and the multivariate ROC curve combining the two features showed a very good AUC (0.900; 95%IC: 0.724-1.000) in discriminating responders from not responders. More heterogeneous tissue texture of initial staging CT in locally advanced or metastatic ACC could predict the positive response to EDP-M treatment.

CONCLUSIONS

Adrenal texture is able to predict the response to EDP-M therapy in patients with advanced ACC.

Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma

Objective

Invasive lung adenocarcinoma（ILA） with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution（SR） enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning.

Methods

Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC).

Result

A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility.

Conclusion

The study successfully constructed and evaluated a deep learning（DL）-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma

BACKGROUND

Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment.

OBJECTIVE

This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers.

DESIGN

A retrospective case control, diagnostic accuracy study.

METHODS

This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines.

RESULTS

The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively.

CONCLUSION

This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

Possibility of Using Conventional Computed Tomography Features and Histogram Texture Analysis Parameters as Imaging Biomarkers for Preoperative Prediction of High-Risk Gastrointestinal Stromal Tumors of the Stomach

BACKGROUND

The objective of this study is to determine the morphological computed tomography features of the tumor and texture analysis parameters, which may be a useful diagnostic tool for the preoperative prediction of high-risk gastrointestinal stromal tumors (HR GISTs).

METHODS

This is a prospective cohort study that was carried out in the period from 2019 to 2022. The study included 79 patients who underwent CT examination, texture analysis, surgical resection of a lesion that was suspicious for GIST as well as pathohistological and immunohistochemical analysis.

RESULTS

Textural analysis pointed out min norm (p = 0.032) as a histogram parameter that significantly differed between HR and LR GISTs, while min norm (p = 0.007), skewness (p = 0.035) and kurtosis (p = 0.003) showed significant differences between high-grade and low-grade tumors. Univariate regression analysis identified tumor diameter, margin appearance, growth pattern, lesion shape, structure, mucosal continuity, enlarged peri- and intra-tumoral feeding or draining vessel (EFDV) and max norm as significant predictive factors for HR GISTs. Interrupted mucosa (p < 0.001) and presence of EFDV (p < 0.001) were obtained by multivariate regression analysis as independent predictive factors of high-risk GISTs with an AUC of 0.878 (CI: 0.797-0.959), sensitivity of 94%, specificity of 77% and accuracy of 88%.

CONCLUSION

This result shows that morphological CT features of GIST are of great importance in the prediction of non-invasive preoperative metastatic risk. The incorporation of texture analysis into basic imaging protocols may further improve the preoperative assessment of risk stratification.

Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma

Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients.

Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics

BACKGROUND

In this study, we used computed tomography (CT)-based radiomics signatures to predict the mutation status of KRAS in patients with colorectal cancer (CRC) and to identify the phase of radiomics signature with the most robust and high performance from triphasic enhanced CT.

METHODS

This study involved 447 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT. They were categorized into training (n = 313) and validation cohorts (n = 134) in a 7:3 ratio. Radiomics features were extracted using triphasic enhanced CT imaging. The Boruta algorithm was used to retain the features closely associated with KRAS mutations. The Random Forest (RF) algorithm was used to develop radiomics, clinical, and combined clinical-radiomics models for KRAS mutations. The receiver operating characteristic curve, calibration curve, and decision curve were used to evaluate the predictive performance and clinical usefulness of each model.

RESULTS

Age, CEA level, and clinical T stage were independent predictors of KRAS mutation status. After rigorous feature screening, four arterial phase (AP), three venous phase (VP), and seven delayed phase (DP) radiomics features were retained as the final signatures for predicting KRAS mutations. The DP models showed superior predictive performance compared to AP or VP models. The clinical-radiomics fusion model showed excellent performance, with an AUC, sensitivity, and specificity of 0.772, 0.792, and 0.646 in the training cohort, and 0.755, 0.724, and 0.684 in the validation cohort, respectively. The decision curve showed that the clinical-radiomics fusion model had more clinical practicality than the single clinical or radiomics model in predicting KRAS mutation status.

CONCLUSION

The clinical-radiomics fusion model, which combines the clinical and DP radiomics model, has the best predictive performance for predicting the mutation status of KRAS in CRC, and the constructed model has been effectively verified by an internal validation cohort.

Radiomics in gastrointestinal stromal tumours: an up-to-date review

Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.

Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients

PURPOSE

It was reported that individual heterogeneity among malignancies (IHAM) might correlate well to the prognosis of lung cancer; however, seldom radiomic study is on this field. Standard deviation (SD) in statistics could scale average amount of variability of a variable; therefore, we used SD of CT feature (FeatureSD) among primary tumor and malignant lymph nodes (LNs) in an individual to represent IHAM, and its prognostic ability was explored.

METHODS

The enrolled patients who had accepted PET/CT scans were selected from our previous study (ClinicalTrials.gov, NCT03648151). The patients had primary tumor and at least one LN, and standardized uptake value of LN higher than 2.0 and 2.5 were enrolled as the cohort 1 (n = 94) and 2 (n = 88), respectively. FeatureSD from the combined or thin-section CT were calculated among primary tumor and malignant LNs in each patient, and were separately selected by the survival XGBoost method. Finally, their prognostic ability was compared to the significant patient characteristics identified by the Cox regression.

RESULTS

In the univariate and multi-variate Cox analysis, surgery, target therapy, and TNM stage were significantly against OS in the both cohorts. In the survival XGBoost analysis of the thin-section CT dataset, none FeatureSD could be repeatably ranked on the top list of the both cohorts. For the combined CT dataset, only one FeatureSD ranked in the top three of both cohorts, but the three significant factors in the Cox regression were not even on the list. Both in the cohort 1 and 2, C-index of the model composed of the three factors could be improved by integrating the continuous FeatureSD; furthermore, that of each factor was obviously lower than FeatureSD.

CONCLUSION

Standard deviation of CT features among malignant foci within an individual was a powerful prognostic factor in vivo for lung cancer patients.

Can CT texture analysis parameters be used as imaging biomarkers for prediction of malignancy in canine splenic tumors?

Splenic hemangiosarcoma has morphological similarities to benign nodular hyperplasia. Computed tomography (CT) texture analysis can analyze the texture of images that the naive human eye cannot detect. Recently, there have been attempts to incorporate CT texture analysis with artificial intelligence in human medicine. This retrospective, analytical design study aimed to assess the feasibility of CT texture analysis in splenic masses and investigate predictive biomarkers of splenic hemangiosarcoma in dogs. Parameters for dogs with hemangiosarcoma and nodular hyperplasia were compared, and an independent parameter that could differentiate between them was selected. Discriminant analysis was performed to assess the ability to discriminate the two splenic masses and compare the relative importance of the parameters. A total of 23 dogs were sampled, including 16 splenic nodular hyperplasia and seven hemangiosarcoma. In each dog, total 38 radiomic parameters were extracted from first-, second-, and higher-order matrices. Thirteen parameters had significant differences between hemangiosarcoma and nodular hyperplasia. Skewness in the first-order matrix and GLRLM_LGRE and GLZLM_ZLNU in the second, higher-order matrix were determined as independent parameters. A discriminant equation consisting of skewness, GLZLM_LGZE, and GLZLM_ZLNU was derived, and the cross-validation verification result showed an accuracy of 95.7%. Skewness was the most influential parameter for the discrimination of the two masses. The study results supported using CT texture analysis to help differentiate hemangiosarcoma from nodular hyperplasia in dogs. This new diagnostic approach can be used for developing future machine learning-based texture analysis tools.

Feature selection methods and predictive models in CT lung cancer radiomics

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

A Combination of Radiomic Features, Imaging Characteristics, and Serum Tumor Biomarkers to Predict the Possibility of the High-Grade Subtypes of Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Lung adenocarcinomas (LADC) containing high-grade subtypes have a poorer prognosis. And some studies have shown that high-grade subtypes have been identified as an independent predictor of local recurrence in patients treated with limited resection. The aim of this study was to construct a combined model based on radiomic features, imaging characteristics and serum tumor biomarkers to predict the possibility of preoperative high-grade subtypes.

MATERIALS AND METHODS

156 patients with LADC were retrospectively recruited in this study. These patients were randomly divided into training and validation cohorts. Radiomics features and imaging characteristics were extracted from plain CT images. A nomogram was developed in a training cohort by univariate and multivariate logistic analysis, and its performance was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in the training and validation cohorts.

RESULTS

A total of 1316 radiomic features were extracted from the lesions in plain chest CT images. After applying the mRMR algorithm and the LASSO regression, 4 features were retained. Based on these radiomic features, Radiomic score (Radscore) was calculated for each patient. Spiculation, air bronchogram sign, CYFRA 21-1 and Radscore had been used in the construction of the combined model. The AUC of the combined model was respectively 0.88 (95% CI, 0.82-0.95) and 0.94 (95% CI, 0.86-1.00) in the training and validation cohorts.

CONCLUSION

The combined model based on CT images and serum tumor biomarkers, can predict the high-grade subtypes of LADC in a non-invasive manner, which may influence individual treatment planning, such as the choice of surgical approach and postoperative adjuvant therapy.

Risk analysis of grade ≥ 2 radiation pneumonitis based on radiotherapy timeline in stage III/IV non-small cell lung cancer treated with volumetric modulated arc therapy: a retrospective study

Background

Radiotherapy is an important treatment for patients with stage III/IV non-small cell lung cancer (NSCLC), and due to its high incidence of radiation pneumonitis, it is essential to identify high-risk people as early as possible. The present work investigates the value of the application of different phase data throughout the radiotherapy process in analyzing risk of grade ≥ 2 radiation pneumonitis in stage III/IV NSCLC. Furthermore, the phase data fusion was gradually performed with the radiotherapy timeline to develop a risk assessment model.

Methods

This study retrospectively collected data from 91 stage III/IV NSCLC cases treated with Volumetric modulated arc therapy (VMAT). Patient data were collected according to the radiotherapy timeline for four phases: clinical characteristics, radiomics features, radiation dosimetry parameters, and hematological indexes during treatment. Risk assessment models for single-phase and stepwise fusion phases were established according to logistic regression. In addition, a nomogram of the final fusion phase model and risk classification system was generated. Receiver operating characteristic (ROC), decision curve, and calibration curve analysis were conducted to internally validate the nomogram to analyze its discrimination.

Results

Smoking status, PTV and lung radiomics feature, lung and esophageal dosimetry parameters, and platelets at the third week of radiotherapy were independent risk factors for the four single-phase models. The ROC result analysis of the risk assessment models created by stepwise phase fusion were: (area under curve [AUC]: 0.67,95% confidence interval [CI]: 0.52–0.81), (AUC: 0.82,95%CI: 0.70–0.94), (AUC: 0.90,95%CI: 0.80–1.00), and (AUC:0.90,95%CI: 0.80–1.00), respectively. The nomogram based on the final fusion phase model was validated using calibration curve analysis and decision curve analysis, demonstrating good consistency and clinical utility. The nomogram-based risk classification system could correctly classify cases into three diverse risk groups: low-(ratio:3.6%; 0 < score < 135), intermediate-(ratio:30.7%, 135 < score < 160) and high-risk group (ratio:80.0%, score > 160).

Conclusions

In our study, the risk assessment model makes it easy for physicians to assess the risk of grade ≥ 2 radiation pneumonitis at various phases in the radiotherapy process, and the risk classification system and nomogram identify the patient’s risk level after completion of radiation therapy.

A primer on texture analysis in abdominal radiology

The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.

Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis

OBJECTIVE

To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules.

MATERIALS AND METHODS

CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep-learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction (HIR) technique. The AIIR data were divided into a training (n = 96) and a validation set (n = 22), and the HIR data were set as the test set (n = 118). Extracted texture features were compared using the Mann-Whitney U test and t-test. The diagnostic performance of the classification model was analyzed with the receiver operating characteristic curve (ROC), the area under ROC (AUC), sensitivity, specificity, and accuracy.

RESULTS

Among the obtained 68 texture features, 51 (75.0%) were not influenced by the change of reconstruction algorithm (p > 0.05). Forty-four features were significantly different between benign and malignant nodules (p < 0.05) for the AIIR dataset, which were selected to build the classification model. The accuracy and AUC of the classification model were 92.3% and 0.91 (95% confidence interval [CI], 0.74-0.90) with the validation set, which were 80.0% and 0.80 (95% CI, 0.68-0.86) with the test set.

CONCLUSION

With the CT texture analysis model trained with deep-learning reconstruction (AIIR) images showing favorable diagnostic accuracy in discriminating benign and malignant pulmonary nodules, it also has certain applicability to the iterative reconstruction (HIR) images.

Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined?

Purpose

This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC).

Methods

After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pre-treatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score.

Results

The radiomic model using features from the peritumoral region of 0–3 mm outperformed that using features from 3–6, 6–9, 9–12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0–3 and 3–6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05).

Conclusions

CT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions.

Vector textures derived from higher order derivative domains for classification of colorectal polyps

Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities. In this study, higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities. To make good use of the derivative information, a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors. Two widely used differential operators, i.e., the gradient operator and Hessian operator, are utilized to generate the first and second order derivative images. These derivative volumetric images are used to produce two angle-based and two vector-based (including both angle and magnitude) textures. Next, a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications. To evaluate the performance of our method, experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography. We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13% evaluated by the area under the receiver operating characteristics curves.

Rectal MRI radiomics inter- and intra-reader reliability: should we worry about that?

PURPOSE

The aim of this review paper is to summarize the current literature regarding inter- and intra-reader reliability of radiomics on rectal MRI.

METHODS

Original studies examining treatment response prediction in patients with rectal cancer following neoadjuvant therapy using rectal MRI-based radiomics between January 2010 and December 2021 were identified via a PubMed/Medline search. Studies in which intra- and/or inter-reader reliability had been reported were included in this review.

RESULTS

Thirteen studies were selected, with an average number of patients of 145 (range, 20-649). All included studies evaluated T2-weighted imaging (T2WI) and/or diffusion-weighted imaging (DWI) sequences, while 3/13 (23%) also evaluated the contrast-enhanced T1-weighted imaging (T1WI) sequence. Most of the selected studies involved two readers (10/13, 77%), 6/13 (46%) studies used baseline MRI only, 1/13 (8%) study used restaging MRI only, and 6/13 (46%) used both. Segmentation was performed manually in 10/13 (77%) studies, and in a slight majority of studies (7/13, 54%), the entire tumor volume (3D VOI) was segmented, while 4/13 (31%) studies segmented the 2D ROI and 2/13 (15%) segmented both. Intraclass correlation coefficient (ICC) on intra-reader agreement varied from 0.73 to 0.93. ICC to assess inter-reader varied from 0.60 to 0.99. Overall, features obtained from baseline rectal MRI, using 3D VOI and first-order features, had higher agreement.

CONCLUSION

Based on our qualitative assessment of a small number of non-dedicated studies, there seems to be good reliability, particularly among low-order features extracted from the entire tumor volume using baseline MRI; however, direct evidence remains scarce. More targeted research in this area is required to quantitatively verify reliability, and before these novel radiomic techniques can be clinically adopted.

Prediction of Two-Year Recurrence-Free Survival in Operable NSCLC Patients Using Radiomic Features from Intra- and Size-Variant Peri-Tumoral Regions on Chest CT Images

To predict the two-year recurrence-free survival of patients with non-small cell lung cancer (NSCLC), we propose a prediction model using radiomic features of the inner and outer regions of the tumor. The intratumoral region and the peritumoral regions from the boundary to 3 cm were used to extract the radiomic features based on the intensity, texture, and shape features. Feature selection was performed to identify significant radiomic features to predict two-year recurrence-free survival, and patient classification was performed into recurrence and non-recurrence groups using SVM and random forest classifiers. The probability of two-year recurrence-free survival was estimated with the Kaplan–Meier curve. In the experiment, CT images of 217 non-small-cell lung cancer patients at stages I-IIIA who underwent surgical resection at the Veterans Health Service Medical Center (VHSMC) were used. Regarding the classification performance on whole tumors, the combined radiomic features for intratumoral and peritumoral regions of 6 mm and 9 mm showed improved performance (AUC 0.66, 0.66) compared to T stage and N stage (AUC 0.60), intratumoral (AUC 0.64) and peritumoral 6 mm and 9 mm classifiers (AUC 0.59, 0.62). In the assessment of the classification performance according to the tumor size, combined regions of 21 mm and 3 mm were significant when predicting outcomes compared to other regions of tumors under 3 cm (AUC 0.70) and 3 cm~5 cm (AUC 0.75), respectively. For tumors larger than 5 cm, the combined 3 mm region was significant in predictions compared to the other features (AUC 0.71). Through this experiment, it was confirmed that peritumoral and combined regions showed higher performance than the intratumoral region for tumors less than 5 cm in size and that intratumoral and combined regions showed more stable performance than the peritumoral region in tumors larger than 5 cm.

Quantitative Measurement of Throat and Larynx After Endotracheal Intubation for Palatoplasty

Introduction

Quantitative morphometric measurements of living human upper airway remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted, digitalized analysis and provide important information on airway mucosa change, before and after endotracheal intubation for palatoplasty.

Materials and Methods

The laryngeal images were captured before and after surgery. Image processing techniques were used to quantize the post-operative laryngeal variation, with its distinct color space and texture features. Meanwhile, the maximum length of the vocal fold, vocal width at the midpoint, maximum cross-sectional area of the glottic space, maximum cross-sectional area of the oropharyngeal inlet (CSAOI) and the depth of the retropalatal space were determined and calculated. These parameters were analyzed and compared before and after surgery.

Results

A total of 39 subjects were enrolled in this study. The color space and texture analysis all show trends toward higher measures in post-operative images than in pre-operative images, especially in the interarytenoid region. Furthermore, the glottic area showed a significant decrease of 31.2%, while the vocal width showed a significant increase after intubation. The post-operative retropalatal depth and CSAOI were significantly deeper and larger than the baseline, reaching their peak in the 4th week after the surgery, and then slightly reduced in the 12th week.

Conclusion

For the first time we present a series of changes in upper airway after surgery, by using a laser module with quantitative measurement. Our equipment and processing can measure subtle mucosal changes that would allow a clinician to diagnose post-operative airway inflammation in a simpler and less invasive way. Here additional information collected by different imaging modalities would help to solve multiple current unmet needs in post-operative airway inflammation.

Incidental Thyroid Nodule on Chest Computed Tomography: Application of Computed Tomography Texture Analysis in Prediction of Ultrasound Classification

OBJECTIVE

The aim of the study was to evaluate the value of computed tomography (CT) texture analysis (CTTA) in predicting ultrasound (US) classification of incidentally detected thyroid nodule (ITN) on chest CT.

METHODS

A total of 117 ITNs (≥1 cm in the longest diameter) on chest CT scan of 107 patients was divided into 4 categories according to the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification on recent thyroid US within 3 months. Computed tomography texture features were extracted with or without filtration using commercial software. The texture features were compared between the benign (K-TIRADS 2; n = 21) and the suspicious (K-TIRADS 3, 4, 5; n = 96) nodules. Multivariate regression and area under the receiver operating characteristic curve analysis were performed to determine significant prediction factors of the suspicious nodules.

RESULTS

The mean value of positive pixels was significantly higher in the suspicious nodules except the unfiltered image (P < 0.05). Entropy of the suspicious nodules was significantly higher with unfiltered and fine filters (P < 0.05), and kurtosis of the suspicious nodules was significantly higher with medium and coarse filters (P < 0.05). A logistic regression model incorporating mean value of positive pixels and kurtosis with a medium filter using volumetric analysis demonstrated the best performance to predict the suspicious nodules with an area under the receiver operating characteristic curve of 0.842 (P < 0.001, sensitivity 82.3%, and specificity 81.0%).

CONCLUSIONS

Computed tomography texture analysis for ITN larger than 1 cm showed significant correlation with systematic thyroid US classification and presented excellent performance to predict the suspicious nodules.

A Combination Analysis of IVIM-DWI Biomarkers and T2WI-Based Texture Features for Tumor Differentiation Grade of Cervical Squamous Cell Carcinoma

Purpose

To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and texture analysis on T2-weighted imaging (T2WI) for evaluating pathological differentiation of cervical squamous cell carcinoma.

Method

This retrospective study included a total of 138 patients with pathologically confirmed poor/moderate/well-differentiated (71/49/18) who underwent conventional MRI and IVIM-DWI scans. The values of ADC, D, D ∗ , and f and 58 T2WI-based texture features (18 histogram features, 24 gray-level co-occurrence matrix features, and 16 gray-level run length matrix features) were obtained. Multiple comparison, correlation, and regression analyses were used.

Results

For IVIM-DWI, the ADC, D, D ∗ , and f were significantly different among the three groups (p < 0.05). ADC, D, and D ∗ were positively correlated with pathological differentiation (r = 0.262, 0.401, 0.401; p < 0.05), while the correlation was negative for f (r = -0.221; p < 0.05). The comparison of 52 parameters of texture analysis on T2WI reached statistically significant levels (p < 0.05). Multivariate logistic regression analysis incorporated significant IVIM-DWI, and texture features on T2WI showed good diagnostic performance both in the four differentiation groups (poorly vs. moderately, area under the curve(AUC) = 0.797; moderately vs. well, AUC = 0.954; poorly vs. moderately and well, AUC = 0.795; and well vs. moderately and poorly, AUC = 0.952). The AUCs of each parameters alone were smaller than that of each regression model (0.503∼0.684, 0.547∼0.805, 0.511∼0.712, and 0.636∼0.792, respectively; pairwise comparison of ROC curves between regression model and individual variables, p < 0.05).

Conclusions

IVIM-DWI biomarkers and T2WI-based texture features had potential to evaluate the pathological differentiation of cervical squamous cell carcinoma. The combination of IVIM-DWI with texture analysis improved the predictive performance.

A CT-Based Radiomics Nomogram to Predict Complete Ablation of Pulmonary Malignancy: A Multicenter Study

Objective

Thermal ablation is a minimally invasive procedure for the treatment of pulmonary malignancy, but the intraoperative measure of complete ablation of the tumor is mainly based on the subjective judgment of clinicians without quantitative criteria. This study aimed to develop and validate an intraoperative computed tomography (CT)-based radiomic nomogram to predict complete ablation of pulmonary malignancy.

Methods

This study enrolled 104 individual lesions from 92 patients with primary or metastatic pulmonary malignancies, which were randomly divided into training cohort (n=74) and verification cohort (n=30). Radiomics features were extracted from the original CT images when the study clinicians determined the completion of the ablation surgery. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted for the dimensionality reduction of high-dimensional data and feature selection. The prediction model was developed based on the radiomics signature combined with the independent clinical predictors by multiple logistic regression analysis. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. Decision curve analysis (DCA) was applied to estimate the clinical usefulness and net benefit of the nomogram for decision making.

Results

Thirteen CT features were selected to construct radiomics prediction model, which exhibits good predictive performance for determination of complete ablation of pulmonary malignancy. The AUCs of a CT-based radiomics nomogram that integrated the radiomics signature and the clinical predictors were 0.88 (95% CI 0.80-0.96) in the training cohort and 0.87 (95% CI: 0.71–1.00) in the validation cohort, respectively. The radiomics nomogram was well calibrated in both the training and validation cohorts, and it was highly consistent with complete tumor ablation. DCA indicated that the nomogram was clinically useful.

Conclusion

A CT-based radiomics nomogram has good predictive value for determination of complete ablation of pulmonary malignancy intraoperatively, which can assist in decision-making.

Pretherapy 18F-fluorodeoxyglucose positron emission tomography/computed tomography robust radiomic features predict overall survival in non-small cell lung cancer

OBJECTIVE

To extract robust radiomic features from staging positron emission tomography/computed tomography (18F- fluroodeoxyglucose PET/CT) in patients with non-small cell lung cancer from different segmentation methods and to assess their association with 2-year overall survival.

METHODS

Eighty-one patients with stage I-IV non-small cell lung cancer were included. All patients underwent a pretherapy 18F-FDG PET/CT. Primary tumors were delineated using four different segmentation methods: method 1, manual; method 2: manual with peripheral 1 mm erosion; method 3: absolute threshold at standardized uptake value (SUV) 2.5; and method 4: relative threshold at 40% SUVmax. Radiomic features from each method were extracted using Image Biomarker Standardization Initiative-compliant process. The study cohort was divided into two groups (exploratory and testing) in a ratio of 1:2 (n = 25 and n = 56, respectively). Exploratory cohort was used to identify robust radiomic features, defined as having a minimum concordance correlation coefficient ≥0.75 among all the 4-segmentation methods. The resulting texture features were evaluated for association with 2-year overall survival in the testing cohort (n = 56). All patients in the testing cohort had a follow-up for 2 years from the date of staging 18F-FDG PET/CT scan or till death. Cox proportional hazard models were used to evaluate the independent prognostic factors.

RESULTS

Exploratory and validation cohorts were equivalent regarding their basic characteristics (age, sex, and tumor stage). Ten radiomic features were deemed robust to the described four segmentation methods: SUV SD, SUVmax, SUVQ3, SUVpeak in 0.5 ml, total lesion glycolysis, histogram entropy log 2, histogram entropy log 10, histogram energy uniformity, gray level run length matrix-gray level non-uniformity, and gray level zone length matrix-gray level non-uniformity. At the end of 2-year follow-up, 41 patients were dead and 15 were still alive (overall survival = 26.8%; median survival = 14.7 months, 95% confidence interval: 10.2-19.2 months). Three texture features, regardless the segmentation method, were associated with 2-year overall survival: total lesion glycolysis, gray level run length matrix_gray level non-uniformity, and gray level zone length matrix_run-length non-uniformity. In the final Cox-regression model: total lesion glycolysis, and gray level zone length matrix_gray level non-uniformity were independent prognostic factors. The quartiles from the two features were combined with clinical staging in a prognostic model that allowed better risk stratification of patients for overall survival.

CONCLUSION

Ten radiomic features were robust to segmentation methods and two of them (total lesion glycolysis and gray level zone length matrix_gray level non-uniformity) were independently associated with 2-year overall survival. Together with the clinical staging, these features could be utilized towards improved risk stratification of lung cancer patients.

The Role of Histogram-Based Textural Analysis of 18F-FDG PET/CT in Evaluating Tumor Heterogeneity and Predicting the Prognosis of Invasive Lung Adenocarcinoma

OBJECTIVES

This study aimed to investigate the contributory role of histogram-based textural features (HBTFs) extracted from 18fluorinefluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) in tumoral heterogeneity (TH) evaluation and invasive lung adenocarcinoma (ILA) prognosis prediction.

METHODS

This retrospective study analyzed the data of 72 patients with ILA who underwent 18F-FDG PET/CT followed by surgical resection. The maximum standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis values were calculated for each tumor. Additionally, HBTFs were extracted from 18F-FDG PET/CT images using the software program. ILA was classified into the following five histopathological subtypes according to the predominant pattern: Lepidic adenocarcinoma (LA), acinar adenocarcinoma, papillary adenocarcinoma, solid adenocarcinoma (SA), and micropapillary adenocarcinoma (MA). Differences between 18F-FDG PET/CT parameters and histopathological subtypes were evaluated using non-parametric tests. The study endpoints include overall survival (OS) and progression-free survival (PFS). The prognostic values of clinicopathological factors and 18F-FDG PET/CT parameters were evaluated using the Cox regression analyses.

RESULTS

The median SUVmax and entropy values were significantly higher in SA-MA, whereas lower in LA. The median energy-uniformity value of the LA was significantly higher than the others. Among all parameters, only skewness and kurtosis were significantly associated with lymph node involvement status. The median values for follow-up time, PFS, and OS were 31.26, 16.07, and 20.87 months, respectively. The univariate Cox regression analysis showed that lymph node involvement was the only significant predictor for PFS. The multivariate Cox regression analysis revealed that higher SUVmax (≥11.69) and advanced stage (IIB-IIIA) were significantly associated with poorer OS [hazard ratio (HR): 3.580, p=0.024 and HR: 7.608, p=0.007, respectively].

CONCLUSION

HBTFs were tightly associated with clinicopathological factors causing TH. Among the 18F-FDG PET/CT parameters, only skewness and kurtosis were associated with lymph node involvement, whereas SUVmax was the only independent predictor of OS. TH measurement with HBTFs may contribute to conventional metabolic parameters in guiding precision medicine for ILA.

Role of CT texture analysis for predicting peritoneal metastases in patients with gastric cancer

PURPOSE

Aim of the study was to perform CT texture analysis in patients with gastric cancer (GC) to investigate potential role of radiomics for predicting the occurrence of peritoneal metastases (PM).

MATERIALS AND METHODS

In this single-centre retrospective study, patients with gastric adenocarcinoma and surgically confirmed presence or absence of PM were, respectively, enrolled in group PM and group non-PM. Patients with T1-staging, previous treatment or presence of imaging artifacts were excluded from the study. Pre-operative CT examinations were evaluated. Acquisition protocol consisted of gastric distension with water, pre-contrast and arterial phases on upper abdomen and portal phase on thorax and whole abdomen. Texture analysis was performed on portal phase images: the region of interest was manually drawn along the margins of the primitive lesion on each slice and the volume of interest of the whole tumour was obtained. A total of 38 texture parameters were extracted and analysed. ROC curves were performed on significant texture features (p < 0.05). Multiple logistic regression was conducted on features with the best AUC to identify differentiating variables for both groups.

RESULTS

A total of 90 patients were evaluated (group PM, n = 45; group non-PM, n = 45). T2/T3 tumours were prevalent in group non-PM, T4 was significantly associated with group PM. Significant differences between the two groups were observed for 22/38 texture parameters. Volume and GLRLM_LRHGE showed the greatest AUC in ROC curve analysis (0.737 and 0.734, respectively) and were found to be independent differentiating variables of group PM in the multiple regression analysis (OR 8.44, [95% CI, 1.52-46.8] and OR 18.99 [95% CI, 84-195.31], respectively).

CONCLUSIONS

Our preliminary results suggest the potential value of CT texture analysis for predicting the risk of PM from GC, which may be helpful to stratify patients and address them to the most appropriate treatment.

Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics

PURPOSE

In the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.

METHODS

A total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.

RESULTS

A total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.

CONCLUSIONS

A prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.

Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Simple Summary

Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine.

Abstract

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

Identification of Stage IIIC/IV EGFR-Mutated Non-Small Cell Lung Cancer Populations Sensitive to Targeted Therapy Based on a PET/CT Radiomics Risk Model

Objectives

This project aimed to construct an individualized PET/CT prognostic biomarker to accurately quantify the progression risk of patients with stage IIIC-IV epidermal growth factor receptor (EGFR)-mutated Non-small cell lung cancer (NSCLC) after first-line first and second generation EGFR- tyrosine kinase inhibitor (TKI) drug therapy and identify the first and second generation EGFR-TKI treatment-sensitive population.

Methods

A total of 250 patients with stage IIIC-IV EGFR-mutated NSCLC underwent first-line first and second generation EGFR-TKI drug therapy were included from two institutions (140 patients in training cohort; 60 patients in internal validation cohort, and 50 patients in external validation cohort). 1037 3D radiomics features were extracted to quantify the phenotypic characteristics of the tumor region in PET and CT images, respectively. A four-step feature selection method was performed to enable derivation of stable and effective signature in the training cohort. According to the median value of radiomics signature score (Rad-score), patients were divided into low- and high-risk groups. The progression-free survival (PFS) behaviors of the two subgroups were compared by Kaplan–Meier survival analysis.

Results

Our results shown that higher Rad-scores were significantly associated with worse PFS in the training (p < 0.0001), internal validation (p = 0.0153), and external validation (p = 0.0006) cohorts. Rad-score can effectively identify patients with a high risk of rapid progression. The Kaplan–Meier survival curves of the three cohorts present significant differences in PFS between the stratified slow and rapid progression subgroups.

Conclusion

The PET/CT-derived Rad-score can realize the precise quantitative stratification of progression risk after first-line first and second generation EGFR-TKI drug therapy for NSCLC and identify EGFR-mutated NSCLC populations sensitive to targeted therapy, which might help to provide precise treatment options for NSCLC.

Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.

Success of extracorporeal shock wave lithotripsy based on CT texture analysis

OBJECTIVE

The aims of the study were to evaluate whether computerised tomography texture analysis (CTTA) based on non-contrast computed tomography (NCCT) has predictive value for the success of extracorporeal-shockwave lithotripsy (ESWL) in upper urinary tract stones (UUTS).

METHODS

This study included 156 of 356 patients undergoing ESWL for UUTS sized 0.5-2 cm from 2015 to 2019. Patients with congenital kidney anomalies, radiolucent stones, multiple stones, treated for upper urinary tract stones previously and lower pole stones were excluded from study. The number of ESWL sessions of the patients was as follows: 78 (50%) patients had 1 session, 30 (19.2%) patients had 2 sessions and 48 (30.8%) patients had >2 sessions. First- and second-order CTTA properties of patients' UUTS were evaluated using texture analysis software (LIFEx Software). Other clinical features such as Hounsfield Unit (HU), initial stone size, body-mass index (BMI) and skin to stone distance (SSD) was recorded. The patients were divided into two groups according to ESWL success. Cases with residual stones larger than 4 mm were considered failed cases.

RESULTS

BMI, the standard deviation of HU, SSD, skewness, kurtosis, entropy and all second-order statistics were found to be statistically different between the two groups except for correlation (P < .05). Multivariate analysis showed longer SSD and four new parameters of CTTA (kurtosis, entropy, dissimilarity and energy by the distribution of pixel grey levels in the UUTS) to be significant predictors for unsuccessful ESWL outcomes. SSD and second-order CTTA properties (dissimilarity and energy) had an area under ROC curve of 0.802, 0.850 and 0.824 at a 95% confidence interval. ESWL success rate in all patients was 76.9%.

CONCLUSION

CTTA can help select patients who will undergo ESWL for upper urinary tract stones. Thus, we can reduce treatment costs and ESWL complications by preventing unnecessary ESWL applications.

Radiomics and Digital Image Texture Analysis in Oncology (Review)

One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology.

The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered.

Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.

Applying arterial enhancement fraction (AEF) texture features to predict the tumor response in hepatocellular carcinoma (HCC) treated with Transarterial chemoembolization (TACE)

Background

To evaluate the application of Arterial Enhancement Fraction (AEF) texture features in predicting the tumor response in Hepatocellular Carcinoma (HCC) treated with Transarterial Chemoembolization (TACE) by means of texture analysis.

Methods

HCC patients treated with TACE in Shengjing Hospital of China Medical University from June 2018 to December 2019 were retrospectively enrolled in this study. Pre-TACE Contrast Enhanced Computed Tomography (CECT) and imaging follow-up within 6 months were both acquired. The tumor responses were categorized according to the modified RECIST (mRECIST) criteria. Based on the CECT images, Region of Interest (ROI) of HCC lesion was drawn, the AEF calculation and texture analysis upon AEF values in the ROI were performed using CT-Kinetics (C.K., GE Healthcare, China). A total of 32 AEF texture features were extracted and compared between different tumor response groups. Multi-variate logistic regression was performed using certain AEF features to build the differential models to predict the tumor response. The Receiver Operator Characteristic (ROC) analysis was implemented to assess the discriminative performance of these models.

Results

Forty-five patients were finally enrolled in the study. Eight AEF texture features showed significant distinction between Improved and Un-improved patients (p < 0.05). In multi-variate logistic regression, 9 AEF texture features were applied into modeling to predict “Improved” outcome, and 4 AEF texture features were applied into modeling to predict “Un-worsened” outcome. The Area Under Curve (AUC), diagnostic accuracy, sensitivity, and specificity of the two models were 0.941, 0.911, 1.000, 0.826, and 0.824, 0.711, 0.581, 1.000, respectively.

Conclusions

Certain AEF heterogeneous features of HCC could possibly be utilized to predict the tumor response to TACE treatment.

Performance of quantitative CT texture analysis in differentiation of gastric tumors

PURPOSE

To examine the computed tomography (CT) images of patients with a diagnosis of gastric tumor by texture analysis and to investigate its place in differential diagnosis.

MATERIALS AND METHODS

Contrast enhanced venous phase CT images of 163 patients with pathological diagnosis of gastric adenocarcinoma (n = 125), gastric lymphoma (n = 12) and gastrointestinal stromal tumors (n = 26) were retrospectively analyzed. Pixel size adjustment, gray-level discretization and gray-level normalization procedures were applied as pre-processing steps. Region of interest (ROI) was determined from the axial slice that represented the largest lesion area and a total of 40 texture features were calculated for each patient. Texture features were compared between the tumor subtypes and between adenocarcinoma grades. Statistically significant texture features were combined into a single parameter by logistic regression analysis. The sensitivity and specificity of these features and the combined parameter were measured to differentiate tumor subtypes by receiver-operating characteristic curve (ROC) analysis.

RESULTS

Classifications between adenocarcinoma versus lymphoma, adenocarcinoma vs. gastrointestinal stromal tumor (GIST) and well-differentiated adenocarcinoma versus poorly differentiated adenocarcinoma using texture features yielded successful results with high sensitivity (98, 91, 96%, respectively) and specificity (75, 77, 80%, respectively).

CONCLUSIONS

CT texture analysis is a non-invasive promising method for classifying gastric tumors and predicting gastric adenocarcinoma differentiation.

Radiomics predicts risk of cachexia in advanced NSCLC patients treated with immune checkpoint inhibitors

BACKGROUND

Approximately 50% of cancer patients eventually develop a syndrome of prolonged weight loss (cachexia), which may contribute to primary resistance to immune checkpoint inhibitors (ICI). This study utilised radiomics analysis of ¹⁸F-FDG-PET/CT images to predict risk of cachexia that can be subsequently associated with clinical outcomes among advanced non-small cell lung cancer (NSCLC) patients treated with ICI.

METHODS

Baseline (pre-therapy) PET/CT images and clinical data were retrospectively curated from 210 ICI-treated NSCLC patients from two institutions. A radiomics signature was developed to predict the cachexia with PET/CT images, which was further used to predict durable clinical benefit (DCB), progression-free survival (PFS) and overall survival (OS) following ICI.

RESULTS

The radiomics signature predicted risk of cachexia with areas under receiver operating characteristics curves (AUCs) ≥ 0.74 in the training, test, and external test cohorts. Further, the radiomics signature could identify patients with DCB from ICI with AUCs≥0.66 in these three cohorts. PFS and OS were significantly shorter among patients with higher radiomics-based cachexia probability in all three cohorts, especially among those potentially immunotherapy sensitive patients with PD-L1-positive status (p < 0.05).

CONCLUSIONS

PET/CT radiomics analysis has the potential to predict the probability of developing cachexia before the start of ICI, triggering aggressive monitoring to improve potential to achieve more clinical benefit.

Predictive Radiomic Models for the Chemotherapy Response in Non-Small-Cell Lung Cancer based on Computerized-Tomography Images

The heterogeneity and complexity of non-small cell lung cancer (NSCLC) tumors mean that NSCLC patients at the same stage can have different chemotherapy prognoses. Accurate predictive models could recognize NSCLC patients likely to respond to chemotherapy so that they can be given personalized and effective treatment. We propose to identify predictive imaging biomarkers from pre-treatment CT images and construct a radiomic model that can predict the chemotherapy response in NSCLC. This single-center cohort study included 280 NSCLC patients who received first-line chemotherapy treatment. Non-contrast CT images were taken before and after the chemotherapy, and clinical information were collected. Based on the Response Evaluation Criteria in Solid Tumors and clinical criteria, the responses were classified into two categories: response (n = 145) and progression (n = 135), then all data were divided into two cohorts: training cohort (224 patients) and independent test cohort (56 patients). In total, 1629 features characterizing the tumor phenotype were extracted from a cube containing the tumor lesion cropped from the pre-chemotherapy CT images. After dimensionality reduction, predictive models of the chemotherapy response of NSCLC with different feature selection methods and different machine-learning classifiers (support vector machine, random forest, and logistic regression) were constructed. For the independent test cohort, the predictive model based on a random-forest classifier with 20 radiomic features achieved the best performance, with an accuracy of 85.7% and an area under the receiver operating characteristic curve of 0.941 (95% confidence interval, 0.898–0.982). Of the 20 selected features, four were first-order statistics of image intensity and the others were texture features. For nine features, there were significant differences between the response and progression groups (p < 0.001). In the response group, three features, indicating heterogeneity, were overrepresented and one feature indicating homogeneity was underrepresented. The proposed radiomic model with pre-chemotherapy CT features can predict the chemotherapy response of patients with non-small cell lung cancer. This radiomic model can help to stratify patients with NSCLC, thereby offering the prospect of better treatment.

Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT

Background

Accurate segmentation of pulmonary nodules on computed tomography (CT) scans plays a crucial role in the evaluation and management of patients with suspicion of lung cancer (LC). When performed manually, not only the process requires highly skilled operators, but is also tiresome and time-consuming. To assist the physician in this task several automated and semi-automated methods have been proposed in the literature. In recent years, in particular, the appearance of deep learning has brought about major advances in the field.

Methods

Twenty-four (12 conventional and 12 based on deep learning) semi-automated-'one-click'-methods for segmenting pulmonary nodules on CT were evaluated in this study. The experiments were carried out on two datasets: a proprietary one (383 images from a cohort of 111 patients) and a public one (259 images from a cohort of 100). All the patients had a positive transcript for suspect pulmonary nodules.

Results

The methods based on deep learning clearly outperformed the conventional ones. The best performance [Sørensen-Dice coefficient (DSC)] in the two datasets was, respectively, 0.853 and 0.763 for the deep learning methods, and 0.761 and 0.704 for the traditional ones.

Conclusions

Deep learning is a viable approach for semi-automated segmentation of pulmonary nodules on CT scans.

A Novel Radiomics Nomogram for the Prediction of Secondary Loss of Response to Infliximab in Crohn's Disease

Purpose

The prediction of the loss of response (LOR) to infliximab (IFX) is crucial for optimizing treatment strategies and shifting biologics. However, a secondary LOR is difficult to predict by endoscopy due to the intestinal stricture, perforation, and fistulas. This study aimed to develop and validate a radiomic nomogram for the prediction of secondary LOR to IFX in patients with Crohn’s disease (CD).

Patients and Methods

A total of 186 biologic-naive patients diagnosed with CD between September 2016 and June 2019 were enrolled. Secondary LOR was determined during week 54. Computed tomography enterography (CTE) texture analysis (TA) features were extracted from lesions and analyzed using LIFEx software. Feature selection was performed by least absolute shrinkage and selection operator (LASSO) and ten-fold cross validation. A nomogram was constructed using multivariable logistic regression, and the internal validation was approached by ten-fold cross validation.

Results

Predictors contained in the radiomics nomogram included three first-order and five second-order signatures. The prediction model presented significant discrimination (AUC, 0.880; 95% CI, 0.816–0.944) and high calibration (mean absolute error of = 0.028). Decision curve analysis (DCA) indicated that the nomogram provided clinical net benefit. Ten-fold cross validation assessed the stability of the nomogram with an AUC of 0.817 and an accuracy of 0.819.

Conclusion

This novel radiomics nomogram provides a predictive tool to assess secondary LOR to IFX in patients with Crohn’s disease. This tool will help physicians decide when to switch therapy.

Tocilizumab effects in COVID-19 pneumonia: role of CT texture analysis in quantitative assessment of response to therapy

Purpose

To evaluate CT and laboratory changes in COVID-19 patients treated with tocilizumab, compared to a control group, throughout a combined semiquantitative and texture analysis of images.

Materials and methods

From March 11 to April 20, 2020, 57 SARS-CoV-2 positive patients were retrospectively compared: group T (n = 30) receiving tocilizumab and group non-T (n = 27) undergoing only antivirals/antimalarials. Chest-CT and laboratory findings were analyzed before and after treatment. CT evaluation included both semiquantitative scoring and texture analysis of all parenchymal lesions. Survival and recovery analyses were also provided with Kaplan–Meier method.

Results

In group T, no significant differences were found for CT score after treatment, while several texture features significantly changed, including mean attenuation (p < 0.0001), skewness (p < 0.0001), entropy (p = 0.0146) and higher-order parameters, suggesting considerable fading of parenchymal lesions. PaO₂/FiO₂ mean value significantly increased after treatment, from 240 ± 93 to 363 ± 107 (p = 0.0003), with parallel decrease in inflammatory biomarkers (CRP, D-dimer and LDH).

In group non-T, CT scoring, texture and laboratory parameters showed significant worsening at follow-up. Findings were clinically associated with opposite trends between two groups, with reduction of severe cases in group T (from 21/30 to 5/30; p < 0.0001) as compared to a significant worsening in group non-T (severe cases increasing from 6/27 to 14/27; p = 0.0473).

Probability of discharge was significantly higher in group T (p < 0.0001), as well as survival rate, although not statistically significant.

Conclusions

Our results suggest the potential role of CT texture analysis for assessing response to treatment in COVID-19 pneumonia, using Tocilizumab, as compared to semiquantitative evaluation, providing insight into the intrinsic parenchymal changes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11547-021-01371-7.

Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy by 18F-FDG PET/CT texture analysis

The differentiation of non-small cell lung cancer (NSCLC) and radiation pneumonitis (RP) is critically essential for selecting optimal clinical therapeutic strategies to manage post carbon-ion radiotherapy (CIRT) in patients with NSCLC. The aim of this study was to assess the ability of ¹⁸F-FDG PET/CT metabolic parameters and its textural image features to differentiate NSCLC from RP after CIRT to develop a differential diagnosis of malignancy and benign lesion. We retrospectively analyzed ¹⁸F-FDG PET/CT image data from 32 patients with histopathologically proven NSCLC who were scheduled to undergo CIRT and 31 patients diagnosed with RP after CIRT. The SUV parameters, metabolic tumor volume (MTV), total lesion glycolysis (TLG) as well as fifty-six texture parameters derived from seven matrices were determined using PETSTAT image-analysis software. Data were statistically compared between NSCLC and RP using Wilcoxon rank-sum tests. Diagnostic accuracy was assessed using receiver operating characteristics (ROC) curves. Several texture parameters significantly differed between NSCLC and RP (p < 0.05). The parameters that were high in areas under the ROC curves (AUC) were as follows: SUV_max, 0.64; GLRLM run percentage, 0.83 and NGTDM coarseness, 0.82. Diagnostic accuracy was improved using GLRLM run percentage or NGTDM coarseness compared with SUV_max (p < 0.01). The texture parameters of ¹⁸F-FDG uptake yielded excellent outcomes for differentiating NSCLC from radiation pneumonitis after CIRT, which outperformed SUV-based evaluation. In particular, GLRLM run percentage and NGTDM coarseness of ¹⁸F-FDG PET/CT images would be appropriate parameters that can offer high diagnostic accuracy.

Comparison of CT Texture Analysis Software Platforms in Renal Cell Carcinoma: Reproducibility of Numerical Values and Association With Histologic Subtype Across Platforms

OBJECTIVE. The purpose of this article is to evaluate interobserver, intraobserver, and interplatform variability and compare the previously established association between texture metrics and tumor histologic subtype using three commercially available CT texture analysis (CTTA) software platforms on the same dataset of large (> 7 cm) renal cell carcinomas (RCCs). MATERIALS AND METHODS. CT-based texture analysis was performed on contrast-enhanced MDCT images of large (> 7 cm) untreated RCCs in 124 patients (median age, 62 years; 82 men and 42 women) using three different software platforms. Using this previously studied cohort, texture features were compared across platforms. Features were correlated with histologic subtype, and strength of association was compared between platforms. Single-slice and volumetric measures from one platform were compared. Values for interobserver and intraobserver variability on a tumor subset (n = 30) were assessed across platforms. RESULTS. Metrics including mean gray-level intensity, SD, and volume correlated fairly well across platforms (concordance correlation coefficient [CCC], 0.66-0.99; mean relative difference [MRD], 0.17-5.97%). Entropy showed high variability (CCC, 0.04; MRD, 44.5%). Mean, SD, mean of positive pixels (MPP), and entropy were associated with clear cell histologic subtype on almost all platforms (p < .05). Mean, SD, entropy, and MPP were highly reproducible on most platforms on both interobserver and intraobserver analysis. CONCLUSION. Select texture metrics were reproducible across platforms and readers, but other metrics were widely variable. If clinical models are developed that use CTTA for medical decision making, these differences in reproducibility of some features across platforms need to be considered, and standardization is critical for more widespread adaptation and implementation.

Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy

Although metabolic intratumoral heterogeneity (ITH) gives important value on treatment responses and prognoses, its association with treatment outcomes have not been reported in gastric cancer (GC). We aimed to evaluate temporal changes in metabolic ITH and the associations with treatment responses, progression-free survival (PFS), and overall survival (OS) in advanced GC patients. Eighty-five patients with unresectable, locally advanced, or metastatic GC were prospectively enrolled before the first-line palliative chemotherapy and underwent [¹⁸F]FDG PET at baseline (TP1) and the first response follow-up evaluation (TP2). Standardized uptake values (SUVs), volumetric parameters, and textural features were evaluated in primary gastric tumor at TP1 and TP2. Of 85 patients, 44 had partial response, 33 had stable disease, and 8 progressed. From TP1 to TP2, metabolic ITH was significantly reduced (P < 0.01), and the degree of the decrease was greater in responders than in non-responders (P < 0.01). Using multiple Cox regression analyses, a low SUV_max at TP2, a high kurtosis at TP2 and larger decreases in the coefficient of variance were associated with better PFS. A low SUV_max at TP2, larger decreases in the metabolic tumor volume and larger decreased in the energy were associated with better OS. Age older than 60 years and responders also showed better OS. An early reduction in metabolic ITH is useful to predict treatment outcomes in advanced GC patients.

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

Purpose

To investigate the impact of staging on differences in glucose metabolic heterogeneity between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) by ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-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 ¹⁸F-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.

A Novel Nomogram Model Based on Cone-Beam CT Radiomics Analysis Technology for Predicting Radiation Pneumonitis in Esophageal Cancer Patients Undergoing Radiotherapy

Purpose

We quantitatively analyzed the characteristics of cone-beam computed tomography (CBCT) radiomics in different periods during radiotherapy (RT) and then built a novel nomogram model integrating clinical features and dosimetric parameters for predicting radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC).

Methods

At our institute, a retrospective study was conducted on 96 ESCC patients for whom we had complete clinical feature and dosimetric parameter data. CBCT images of each patient in three different periods of RT were obtained, the images were segmented using both lungs as the region of interest (ROI), and 851 image features were extracted. The least absolute shrinkage selection operator (LASSO) was applied to identify candidate radiomics features, and logistic regression analyses were applied to construct the rad-score. The optimal period for the rad-score, clinical features, and dosimetric parameters were selected to construct the nomogram model and then the receiver operating characteristic (ROC) curve was used to evaluate the prediction capacity of the model. Calibration curves and decision curves were used to demonstrate the discriminatory and clinical benefit ratios, respectively.

Results

The relative volume of total lung treated with ≥5 Gy (V5), mean lung dose (MLD), and tumor stage were independent predictors of RP and were finally incorporated into the nomogram. When the three time periods were modeled, the first period was better than the others. In the primary cohort, the area under the ROC curve (AUC) was 0.700 (95% confidence interval (CI) 0.568–0.832), and in the independent validation cohort, the AUC was 0.765 (95% CI 0.588–0.941). In the nomogram model that integrates clinical features and dosimetric parameters, the AUC in the primary cohort was 0.836 (95% CI 0.700–0.918), and the AUC in the validation cohort was 0.905 (95% CI 0.799–1.000). The nomogram model exhibits excellent performance. Calibration curves indicate a favorable consistency between the nomogram prediction and the actual outcomes. The decision curve exhibits satisfactory clinical utility.

Conclusion

The radiomics model based on early lung CBCT is a potentially valuable tool for predicting RP. V5, MLD, and tumor stage have certain predictive effects for RP. The developed nomogram model has a better prediction ability than any of the other predictors and can be used as a quantitative model to predict RP.

Outcomes of superior segmentectomy versus lower lobectomy for superior segment Stage I non-small-cell lung cancer are equivalent: An analysis of 196 patients at a single, high volume institution

OBJECTIVES

To determine if superior segmentectomy has equivalent overall (OS), disease-free (DFS), and locoregional-recurrence-free survival (LRFS) to lower lobectomy for early-stage non-small-cell lung cancer (NSCLC) in the superior segment.

METHODS

We retrospectively reviewed all Stage 1 lower lobectomies for superior segment lesions and superior segmentectomies at our hospital from 2000 to 2018. Comparison statistics and Cox hazard modeling were performed to determine differences between groups and attempt to identify risk factors for OS, DFS, and LRFS.

RESULTS

Superior segmentectomy patients, compared with lower lobectomy patients, had more current smokers, worse forced expiratory volume in 1 s percentage, radiologic emphysema scores, clinically and pathologically smaller tumors, and more occurrences of 0 lymph nodes examined. Outcomes for superior segmentectomy compared with lower lobectomy were equivalent for 5-year OS (67.0% vs. 75.1%, p = 0.70), DFS (56.9% vs. 60.4%, p = 0.59), and LRFS (87.9% vs. 91.3%, p = 0.46). Multivariable Cox modeling lacked utility due to no outcome differences.

CONCLUSIONS

In well-selected patients, superior segmentectomies can have equivalent OS, DFS, and LRFS compared with lower lobectomies of superior segment tumors for early stage lung cancer. Further data are needed to provide better risk estimates.

Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?

OBJECTIVES

We aimed at investigating the origin of the correlations between tumor volume and ¹⁸F-FDG-PET texture indices in lung cancer.

METHODS

Eighty-five consecutive patients with newly diagnosed non-small cell lung cancer (NSCLC) underwent a ¹⁸F-FDG-PET/CT scan before treatment. Seven phantom spheres uniformly filled with ¹⁸F-FDG, and covering a range of activities and volumes similar to that found in lung tumors, were also scanned. Established texture indices were computed for lung tumors and homogeneous spheres. The dependence between textural indices and volume in homogeneous spheres was modeled and then used to predict texture indices in lung tumors. Correlation analyses were carried out between predicted and texture features measured in lung tumors. Cox proportional hazards regression was used to investigate the associations between overall survival and volume-adjusted textural features.

RESULTS

All textural features showed strong, non-linear correlations with volume, both in tumors and homogeneous spheres. Correlations between predicted versus measured texture features were very high for contrast (r² = 0.91), dissimilarity (r² = 0.90), ZP (r² = 0.90), GLNN (r² = 0.86), and homogeneity (r² = 0.82); high for entropy (r² = 0.50) and HILAE (r² = 0.53); and low for energy (r² = 0.30). Cox regressions showed that among volume-adjusted features, only HILAE was associated with overall survival (b = - 0.35, p = 0.008).

CONCLUSION

We have shown that texture indices previously found to be correlated with a number of clinically relevant outcomes might not provide independent information apart from that driven by their correlation with tumor volume, suggesting that these metrics might not be suitable as intratumor heterogeneity markers.

KEY POINTS

• Associations between texture FDG-PET indices and overall survival have been widely reported in lung cancer, with tumor volume also being associated with overall survival, and therefore, it is still unclear whether the predictive power of textural indices is simply driven by this correlation. • Our results demonstrated strong non-linear correlations between textural indices and volume, showing an analogous behavior for lung tumors from patients and homogeneous spheres inserted in phantoms. • Our findings showed that texture FDG-PET indices might not provide independent information apart from that driven by their correlation with tumor volume.

Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship

PURPOSE

Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm.

METHODS

To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100 mAs/120 kVp) to ultralow dose (1 mAs/120 kVp) by using quantitative texture-based evaluation metrics.

RESULTS

Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7 × 7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve.

CONCLUSIONS

This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.