Association between textural and morphological tumor indices on baseline PET-CT and early metabolic response on interim PET-CT in bulky malignant lymphomas

Lymphoma: The Added Value of Radiomics, Volumes and Global Disease Assessment

Lymphoma represents a condition that holds promise for cure with existing treatment modalities; nonetheless, the primary clinical obstacle lies in advancing therapeutic outcomes by pinpointing high-risk individuals who are unlikely to respond favorably to standard therapy. In this article, the authors will delineate the significant strides achieved in the lymphoma field, with a particular emphasis on the 3 prevalent subtypes: Hodgkin lymphoma, diffuse large B-cell lymphomas, and follicular lymphoma.

Texture analysis of 18F-FDG PET/CT and CECT: Prediction of refractoriness of Hodgkin lymphoma with mediastinal bulk involvement

To recognize patients at high risk of refractory disease, the identification of novel prognostic parameters improving stratification of newly diagnosed Hodgkin Lymphoma (HL) is still needed. This study investigates the potential value of metabolic and texture features, extracted from baseline 18F-FDG Positron Emission Tomography/Computed Tomography (PET) and Contrast-Enhanced Computed Tomography scan (CECT), together with clinical data, in predicting first-line therapy refractoriness (R) of classical HL (cHL) with mediastinal bulk involvement. We reviewed 69 cHL patients who underwent staging PET and CECT. Lesion segmentation and texture parameter extraction were performed using the freeware software LIFEx 6.3. The prognostic significance of clinical and imaging features was evaluated in relation to the development of refractory disease. Receiver operating characteristic curve, Cox proportional hazard regression and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate their prognostic value. Among clinical characteristics, only stage according to the German Hodgkin Group (GHSG) classification system significantly differed between R and not-R. Among CECT variables, only parameters derived from second order matrices (gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) demonstrated significant prognostic power. Among PET variables, SUVmean, several variables derived from first (histograms, shape), and second order analyses (GLCM, GLRLM, NGLDM) exhibited significant predictive power. Such variables obtained accuracies greater than 70% at receiver operating characteristic analysis and their PFS curves resulted statistically significant in predicting refractoriness. At multivariate analysis, only HISTO_EntropyPET extracted from PET (HISTO_EntropyPET ) and GHSG stage resulted as significant independent predictors. Their combination identified 4 patient groups with significantly different PFS curves, with worst prognosis in patients with higher HISTO_EntropyPET values, regardless of the stage. Imaging radiomics may provide a reference for prognostic evaluation of patients with mediastinal bulky cHL. The best prognostic value in the prediction of R versus not-R disease was reached by combining HISTO_EntropyPET with GHSG stage.

Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells

BACKGROUND

In our current work, an 18F-FDG PET/CT radiomics-based model was developed to assess the progression-free survival (PFS) and overall survival (OS) of patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received chimeric antigen receptor (CAR)-T cell therapy.

METHODS

A total of 61 DLBCL cases receiving 18F-FDG PET/CT before CAR-T cell infusion were included in the current analysis, and these patients were randomly assigned to a training cohort (n = 42) and a validation cohort (n = 19). Radiomic features from PET and CT images were obtained using LIFEx software, and radiomics signatures (R-signatures) were then constructed by choosing the optimal parameters according to their PFS and OS. Subsequently, the radiomics model and clinical model were constructed and validated.

RESULTS

The radiomics model that integrated R-signatures and clinical risk factors showed superior prognostic performance compared with the clinical models in terms of both PFS (C-index: 0.710 vs. 0.716; AUC: 0.776 vs. 0.712) and OS (C-index: 0.780 vs. 0.762; AUC: 0.828 vs. 0.728). For validation, the C-index of the two approaches was 0.640 vs. 0.619 and 0.676 vs. 0.699 for predicting PFS and OS, respectively. Moreover, the AUC was 0.886 vs. 0.635 and 0.778 vs. 0.705, respectively. The calibration curves indicated good agreement, and the decision curve analysis suggested that the net benefit of radiomics models was higher than that of clinical models.

CONCLUSIONS

PET/CT-derived R-signature could be a potential prognostic biomarker for R/R DLBCL patients undergoing CAR-T cell therapy. Moreover, the risk stratification could be further enhanced when the PET/CT-derived R-signature was combined with clinical factors.

Radiomics predictive modeling from dual-time-point FDG PET Ki parametric maps: application to chemotherapy response in lymphoma

BACKGROUND

To investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [¹⁸F]FDG PET metabolic uptake rate K_i parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients.

METHODS

We analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP K_i parametric maps. Spearman's rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets.

RESULTS

Via Spearman's rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann-Whitney test).

CONCLUSIONS

Our results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients.

Baseline 18F-FDG PET/CT Radiomics in Classical Hodgkin's Lymphoma: The Predictive Role of the Largest and the Hottest Lesions

This study investigated the predictive role of baseline 18F-FDG PET/CT (bPET/CT) radiomics from two distinct target lesions in patients with classical Hodgkin's lymphoma (cHL). cHL patients examined with bPET/CT and interim PET/CT between 2010 and 2019 were retrospectively included. Two bPET/CT target lesions were selected for radiomic feature extraction: Lesion_A, with the largest axial diameter, and Lesion_B, with the highest SUVmax. Deauville score at interim PET/CT (DS) and 24-month progression-free-survival (PFS) were recorded. Mann-Whitney test identified the most promising image features (p < 0.05) from both lesions with regards to DS and PFS; all possible radiomic bivariate models were then built through a logistic regression analysis and trained/tested with a cross-fold validation test. The best bivariate models were selected based on their mean area under curve (mAUC). A total of 227 cHL patients were included. The best models for DS prediction had 0.78 ± 0.05 maximum mAUC, with a predominant contribution of Lesion_A features to the combinations. The best models for 24-month PFS prediction reached 0.74 ± 0.12 mAUC and mainly depended on Lesion_B features. bFDG-PET/CT radiomic features from the largest and hottest lesions in patients with cHL may provide relevant information in terms of early response-to-treatment and prognosis, thus representing an earlier and stronger decision-making support for therapeutic strategies. External validations of the proposed model are planned.

Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques

BACKGROUND

This study tested the diagnostic value of 18F-FDG PET/CT (FDG-PET) volumetric and texture parameters in the histological differentiation of mediastinal bulky disease due to classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL), using machine learning techniques.

METHODS

We reviewed 80 cHL, 29 PMBCL and 8 GZL adult patients with mediastinal bulky disease and histopathological diagnoses who underwent FDG-PET pre-treatment. Volumetric and radiomic parameters were measured using FDG-PET both for bulky lesions (BL) and for all lesions (AL) using LIFEx software (threshold SUV ≥ 2.5). Binary and multiclass classifications were performed with various machine learning techniques fed by a relevant subset of radiomic features.

RESULTS

The analysis showed significant differences between the lymphoma groups in terms of SUVmax, SUVmean, MTV, TLG and several textural features of both first- and second-order grey level. Among machine learning classifiers, the tree-based ensembles achieved the best performance both for binary and multiclass classifications in histological differentiation.

CONCLUSIONS

Our results support the value of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma.

Classical Hodgkin Lymphoma: A Joint Clinical and PET Model to Predict Poor Responders at Interim Assessment

(1) This study aimed to investigate whether baseline clinical and Positron Emission Tomography/Computed Tomography (bPET)-derived parameters could help predicting early response to the first two cycles of chemotherapy (Deauville Score at interim PET, DS at iPET) in patients with classical Hodgkin lymphoma (cHL) to identify poor responders (DS ≥ 4) who could benefit from first-line treatment intensification at an earlier time point. (2) cHL patients with a bPET and an iPET imaging study in our Centre’s records (2013−2019), no synchronous/metachronous tumors, no major surgical resection of disease prior to bPET, and treated with two cycles of ABVD chemotherapy before iPET were retrospectively included. Baseline International Prognostic Score for HL (IPS) parameters were collected. Each patient’s bPET total metabolic tumor volume (TMTV) and highest tumoral SUVmax were collected. ROC curves and Youden’s index were used to derive the optimal thresholds of TMTV and SUVmax with regard to the DS (≥4). Chi-square or Fisher’s exact test were used for the univariate analysis. A multivariate analysis was then performed using logistic regression. The type I error rate in the hypothesis testing was set to 5%. (3) A total of 146 patients were included. The optimal threshold to predict a DS ≥ 4 was >177 mL for TMTV and >14.7 for SUVmax (AUC of 0.65 and 0.58, respectively). The univariate analysis showed that only TMTV, SUVmax, advanced disease stage, and age were significantly associated with a DS ≥ 4. A multivariate model was finally derived from TMTV, SUVmax, and age, with an AUC of 0.77. (4) A multivariate model with bPET parameters and age at diagnosis was satisfactorily predictive of poor response at iPET after ABVD induction chemotherapy in cHL patients. More studies are needed to validate these results and further implement DS-predictive factors at baseline in order to prevent poor response and intensify therapeutic strategies a-priori when needed.

18F-FDG PET/CT prognostic role in predicting response to salvage therapy in relapsed/refractory Hodgkin's lymphoma

PURPOSE

To evaluate the response predictors, both clinical and ¹⁸F-FDG PET/CT parameters, in Hodgkin's lymphoma (HL) patients diagnosed with refractory/relapsed disease who were planned to receive salvage therapy.

METHODS

In this retrospective study, all HL patients referred to our center between March 2015 and July 2021 were reviewed. Patients with refractory/relapsed disease who were candidates for salvage therapy were included. ¹⁸F-FDG PET/CT measurements at the time of diagnosis were extracted as the predictors, and the lesions' response at the end of the salvage therapy was considered the outcomes. The Kaplan-Meier method and multiple Cox regression were utilized to find the significant parameters to predict the time to reach the complete response. The statistical significance level was set at a two-sided p-value <0.05.

RESULTS

A total of 303 tumoral lesions from 64 patients were included. Regarding the factors associated with the response, B symptoms (p-value < 0.01), pathologic subtype (p-value < 0.001), and patient stage (p-value < 0.01) were the significant clinical parameters. In addition, SUVmax (p-value = 0.03), SUVmax/hepatic background SUVmax (p-value = 0.04), SUVmean (in all thresholds; 41% p-value = 0.02, 51% p-value = 0.04, 61% p-value = 0.01), and MTV (in all thresholds; 41% p-value = 0.04, 51% p-value = 0.04, 61% p-value = 0.05) were the significant parameters in the ¹⁸F-FDG PET/CT scans. At the median follow-up of 9 months, we found that pathologic subtype (p-value < 0.01), patient stage (p-value = 0.03), SUVmax (p-value = 0.02), SUVmax/hepatic background SUVmax (p-value = 0.03), SUVmean (in all thresholds; 41% p-value = 0.01, 51% p-value = 0.02, 61% p-value = 0.02), and MTV ≥ 41% (p-value = 0.02) were significant predictive factors. Multiple Cox regression showed the pathologic subtype (p-value = 0.02), SUVmax (p-value = 0.02), and MTV ≥ 41% (p-value = 0.04) were the most significant predictors.

CONCLUSION

Our study demonstrated that by knowing the histopathology of the lesions, the pre-treatment ¹⁸F-FDG PET/CT might be able to predict response after salvage therapy in the relapsed/refractory HL.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA

Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT)-derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression-free survival (PFS) and overall survival (OS) predictions. Baseline FDG-PET scans were available for 1263 patients, 832 patients of these were cell-of-origin (COO)-evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low-, intermediate- and high-risk groups. The random forest model with COO subgroups identified a clearer high-risk population (45% 2-year PFS [95% confidence interval (CI) 40%-52%]; 65% 2-year OS [95% CI 59%-71%]) than the IPI (58% 2-year PFS [95% CI 50%-67%]; 69% 2-year OS [95% CI 62%-77%]). This study confirms that standard clinical risk factors can be combined with PET-derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.

Clinical Perspectives for 18F-FDG PET Imaging in Pediatric Oncology: Μetabolic Tumor Volume and Radiomics

Pediatric cancer, although rare, requires the most optimized treatment approach to obtain high survival rates and minimize serious long-term side effects in early adulthood. 18F-FDG PET/CT is most helpful and widely used in staging, recurrence detection, and response assessment in pediatric oncology. The well-known 18F-FDG PET metabolic indices of metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG) have already revealed an independent significant prognostic value for survival in oncologic patients, although the corresponding cut-off values remain study-dependent and not validated for use in clinical practice. Advanced tumor “radiomic” analysis sheds new light into these indices. Numerous patterns of texture 18F-FDG uptake features can be extracted from segmented PET tumor images due to new powerful computational systems supporting complex “deep learning” algorithms. This high number of “quantitative” tumor imaging data, although not decrypted in their majority and once standardized for the different imaging systems and segmentation methods, could be used for the development of new “clinical” models for specific cancer types and, more interestingly, for specific age groups. In addition, data from novel techniques of tumor genome analysis could reveal new genes as biomarkers for prognosis and/or targeted therapies in childhood malignancies. Therefore, this ever-growing information of “radiogenomics”, in which the underlying tumor “genetic profile” could be expressed in the tumor-imaging signature of “radiomics”, possibly represents the next model for precision medicine in pediatric cancer management. This paper reviews 18F-FDG PET image segmentation methods as applied to pediatric sarcomas and lymphomas and summarizes reported findings on the values of metabolic and radiomic features in the assessment of these pediatric tumors.

Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions)

Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.

Diagnostic value of baseline 18FDG PET/CT skeletal textural features in follicular lymphoma

At present, ¹⁸F-fluorodesoxyglucose (¹⁸FDG) positron emission tomography (PET)/computed tomography (CT) cannot be used to omit a bone marrow biopsy (BMB) among initial staging procedures in follicular lymphoma (FL). The additional diagnostic value of skeletal textural features on baseline ¹⁸FDG-PET/CT in diffuse large B-cell lymphoma (DLBCL) patients has given promising results. The aim of this study is to evaluate the value of ¹⁸FDG-PET/CT radiomics for the diagnosis of bone marrow involvement (BMI) in FL patients. This retrospective bicentric study enrolled newly diagnosed FL patients addressed for baseline ¹⁸FDG PET/CT. For visual assessment, examinations were considered positive in cases of obvious bone focal uptakes. For textural analysis, the skeleton volumes of interest (VOIs) were automatically extracted from segmented CT images and analysed using LifeX software. BMB and visual assessment were taken as the gold standard: BMB −/PET − patients were considered as bone-_NEGATIVE patients, whereas BMB +/PET −, BMB −/PET + and BMB +/PET + patients were considered bone-_POSITIVE patients. A LASSO regression algorithm was used to select features of interest and to build a prediction model. Sixty-six consecutive patients were included: 36 bone-_NEGATIVE (54.5%) and 30 bone-_POSITIVE (45.5%). The LASSO regression found variance_{_GLCM}, correlation_{_GLCM}, joint entropy_{_GLCM} and busyness_{_NGLDM} to have nonzero regression coefficients. Based on ROC analysis, a cut-off equal to − 0.190 was found to be optimal for the diagnosis of BMI using PET pred.score. The corresponding sensitivity, specificity, PPV and NPV values were equal to 70.0%, 83.3%, 77.8% and 76.9%, respectively. When comparing the ROC AUCs with using BMB alone, visual PET assessment or PET pred.score, a significant difference was found between BMB versus visual PET assessments (p = 0.010) but not between BMB and PET pred.score assessments (p = 0.097). Skeleton texture analysis is worth exploring to improve the performance of ¹⁸FDG-PET/CT for the diagnosis of BMI at baseline in FL patients.

Whole-body MRI radiomics model to predict relapsed/refractory Hodgkin Lymphoma: A preliminary study

PURPOSE

A strong prognostic score that enables a stratification of newly diagnosed Hodgkin Lymphoma (HL) to identify patients at high risk of refractory/relapsed disease is still needed. Our aim was to investigate the potential value of a radiomics analysis pipeline from whole-body MRI (WB-MRI) exams for clinical outcome prediction in patients with HL.

MATERIALS AND METHODS

Index lesions from baseline WB-MRIs of 40 patients (22 females; mean age 31.7 ± 11.4 years) with newly diagnosed HL treated by ABVD chemotherapy regimen were manually segmented on T1-weighted, STIR, and DWI images for texture analysis feature extraction. A machine learning approach based on the Extra Trees classifier and incorporating clinical variables, ¹⁸F-FDG-PET/CT-derived metabolic tumor volume, and WB-MRI radiomics features was tested using cross-validation to predict refractory/relapsed disease.

RESULTS

Relapsed disease was observed in 10/40 patients (25%), two of whom died due to progression of disease and graft versus host disease, while eight reached the complete remission. In total, 1403 clinical and radiomics features were extracted, of which 11 clinical variables and 171 radiomics parameters from both original and filtered images were selected. The 3 best performing Extra Trees classifier models obtained an equivalent highest mean accuracy of 0.78 and standard deviation of 0.09, with a mean AUC of 0.82 and standard deviation of 0.08.

CONCLUSIONS

Our preliminary results demonstrate that a combined machine learning and texture analysis model to predict refractory/relapsed HL on WB-MRI exams is feasible and may help in the clinical outcome prediction in HL patients.

FDG-PET/CT in Lymphoma: Where Do We Go Now?

18F-fluorodeoxyglucose positron emission tomography combined with computed tomography (FDG-PET/CT) is an essential part of the management of patients with lymphoma at staging and response evaluation. Efforts to standardize PET acquisition and reporting, including the 5-point Deauville scale, have enabled PET to become a surrogate for treatment success or failure in common lymphoma subtypes. This review summarizes the key clinical-trial evidence that supports PET-directed personalized approaches in lymphoma but also points out the potential place of innovative PET/CT metrics or new radiopharmaceuticals in the future.

Computed Tomography Imaging Characteristics: Potential Indicators of Epidermal Growth Factor Receptor Mutation in Lung Adenocarcinoma

PURPOSE

The purpose of this study was to investigate the correlation between computed tomography imaging characteristics in lung adenocarcinoma and epidermal growth factor receptor (EGFR) mutations.

METHODS

A total of 124 patients with lung adenocarcinoma and known EGFR mutation status were collected in this retrospective study. Computed tomography quantitative parameters of each tumor, including total volume, total surface, surface-to-volume ratio (SVR), average diameter, maximum diameter, and average density, were determined using computer-aided detection software. The correlation between the EGFR mutation status and imaging characteristics was assessed. The predictive value of these imaging characteristics for EGFR mutation was calculated using the area under the receiver operating characteristic curve.

RESULT

Fifty-eight of 124 patients showed EGFR mutations. Patients who are female (P < 0.001) and nonsmokers (P < 0.001) and those with serum carcinoembryonic antigen (CEA) level of ≥5 (P = 0.035) were likely to have EGFR mutation. Computed tomography features including air bronchogram (P = 0.035), absence of cavitation (P = 0.010), and absence of pulmonary emphysema (P = 0.002) and quantitative parameters, such as smaller total surface (P = 0.002), smaller total volume (P = 0.001), higher SVR (P = 0.003), and smaller average diameter (P = 0.001), were associated with EGFR mutation. Logistic regression analysis revealed that the most significant independent prognostic factors of EGFR mutation for the model were nonsmoking (P = 0.035), CEA level of ≥5 (P = 0.004), presence of air bronchogram (P = 0.040), absence of cavitation (P = 0.021), and high SVR (P = 0.014). The area under the receiver operating characteristic curve, sensitivity, and specificity of the model for predicting EGFR mutation were 0.827, 75.8%, and 82.8%, respectively.

CONCLUSIONS

EGFR-mutated adenocarcinoma showed significantly increased CEA level, presence of air bronchogram, absence of cavitation, and higher quantitative parameter SVR than those with wild-type EGFR.

Prognostic value of baseline metabolic tumor volume in children and adolescents with intermediate-risk Hodgkin lymphoma treated with chemo-radiation therapy: FDG-PET parameter analysis in a subgroup from COG AHOD0031

BACKGROUND

Positron emission tomography (PET)-based measures of baseline total-body tumor burden may improve risk stratification in intermediate-risk Hodgkin lymphoma (HL).

MATERIALS AND METHODS

Evaluable patients were identified from a cohort treated homogeneously with the same combined modality regimen on the Children's Oncology Group AHOD0031 study. Eligible patients had high-quality baseline PET scans. Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were each measured based on 15 thresholds for every patient. Univariate and multivariable Cox regression and Kaplan-Meier survival analyses assessed for an association of MTV and TLG with event-free survival (EFS).

RESULTS

From the AHOD0031 cohort (n = 1712), 86 patients were identified who (i) were treated with four cycles of doxorubicin, bleomycin, vincristine, etoposide, prednisone, cyclophosphamide (ABVE-PC) chemotherapy followed by involved field radiotherapy, and (ii) had a baseline PET scan that was amenable to quantitative analysis. Based on univariate Cox regression analysis, six PET-derived parameters were significantly associated with EFS. For each of these, Kaplan-Meier analyses and the log-rank test were used to compare patients with highest tumor burden (i.e., highest 15%) to the remainder of the cohort. EFS was significantly associated with all six PET parameters (all p < .029). In a multivariable model controlling for important covariates including disease bulk and response to chemotherapy, MTV_2BP was significantly associated with EFS (p = .012).

CONCLUSION

Multiple baseline PET-derived volumetric parameters were associated with EFS. MTV_2BP was highly associated with EFS when controlling for disease bulk and response to chemotherapy. Incorporation of baseline MTV into risk-based treatment algorithms may improve outcomes in intermediate-risk HL.

Overview and recent advances in PET/CT imaging in lymphoma and multiple myeloma

Imaging in hematological diseases has evolved extensively over the past several decades. Positron emission tomography/computed tomography (PET/CT) with of 2-[18 F]-fluoro-2-deoxy-d-glucose ([18 F] FDG) is currently essential for accurate staging and for early and late therapy response assessment for all FDG-avid lymphoproliferative histologies. The widely adopted visual Deauville 5-point scale and Lugano Classification recommendations have recently standardized PET scans interpretation and improved lymphoma patient management. In addition [18 F] FDG-PET is routinely recommended for initial evaluation and treatment response assessment of Multiple Myeloma (MM) with significant contribution in risk-stratification and prognostication, although magnetic resonance imaging remains the Gold Standard for the assessment of bone marrow involvement. In this review, an overview of the role of [18 F] FDG-PET, in hematological malignancies is provided, particularly focusing on Hodgkin lymphoma (HL) and Diffuse Large B Cell Lymphoma (DLBCL), both in adult and pediatric populations, and MM, at each point of patient management. Potential alternative molecular imaging applications in this field, such as non-[18 F] FDG-tracers, whole body magnetic resonance imaging (WB-MRI), hybrid PET/MRI and emerging radiomics research are briefly presented.

Additional value of volumetric and texture analysis on FDG PET assessment in paediatric Hodgkin lymphoma: an Italian multicentric study protocol

INTRODUCTION

Assessment of response to therapy in paediatric patients with Hodgkin lymphoma (HL) by 18F-fluorodeoxyglucose positron emission tomography/CT has become a powerful tool for the discrimination of responders from non-responders. The addition of volumetric and texture analyses can be regarded as a valuable help for disease prognostication and biological characterisation. Based on these premises, the Hodgkin Lymphoma Study Group of the Associazione Italiana Ematologia Oncologia Pediatrica (AIEOP) has designed a prospective evaluation of volumetric and texture analysis in the Italian cohort of patients enrolled in the EuroNet-PHL-C2.

METHODS AND ANALYSIS

The primary objective is to compare volumetric assessment in patiens with HL at baseline and during the course of therapy with standard visual and semiquantitative analyses. The secondary objective is to identify the impact of volumetric and texture analysis on bulky masses. The tertiary objective is to determine the additional value of multiparametric assessment in patients having a partial response on morphological imaging.The overall cohort of the study is expected to be round 400-500 patients, with approximately half presenting with bulky masses. All PET scans of the Italian cohort will be analysed for volumetric assessment, comprising metabolic tumour volume and total lesion glycolysis at baseline and during the course of therapy. A dedicated software will delineate semiautomatically contours using different threshold methods, and the impact of each segmentation techniques will be evaluated. Bulky will be defined on contiguous lymph node masses ≥200 mL on CT/MRI. All bulky masses will be outlined and analysed by the same software to provide textural features. Morphological assessment will be based on RECIL 2017 for response definition.

ETHICS AND DISSEMINATION

The current study has been ethically approved (AIFA/SC/P/27087 approved 09/03/2018; EudraCT 2012-004053-88, EM-04). The results of the different analyses performed during and after study completion the will be actively disseminated through peer-reviewed journals, conference presentations, social media, print media and internet.

A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective.

Prognostic value of imaging markers from 18FDG-PET/CT in paediatric patients with Hodgkin lymphoma

OBJECTIVE

Identification of imaging prognostic parameters for early therapy personalisation to reduce treatment-related morbidity in paediatric Hodgkin lymphoma (HL). Our aim was to evaluate quantitative markers from baseline 2-[18F]fluoro-2-deoxy-d-glucose PET/CT as prognostic factors for treatment outcomes. Another goal was assessing the prognostic value of Deauville score at interim PET/CT.

METHODS

Twenty-one patients were prospectively enrolled. Median age was 12 years (range 6-17); 13 were female. Patients underwent PET/CT for disease staging (bPET), at the end of two cycles of chemotherapy (iPET) and after chemotherapy. A total of 173 lesions were segmented from bPET. We calculated 51 texture features for each lesion. Total metabolic tumour volume and total lesion glycolysis from bPET were calculated for response prediction at iPET. Univariate and multivariate analyses were used for optimal cut-off values to separate responders at iPET according to the Deauville score.

RESULTS

We identified four texture features as possible independent predictors of treatment outcomes at iPET. The areas under the ROC for univariate analysis were 0.89 (95% CI, 0.75-1), 0.82 (95% CI, 0.64-1), 0.79 (95% CI, 0.59-0.99) and 0.89 (95% CI, 0.75-1). The survival curves for patients assigned Deauville scores 1, 2, 3 and X were different from those assigned a score 4, with 4-year progression free-survival (PFS) rates of 85 versus 29%, respectively (P = 0.05).

CONCLUSIONS

We found four textural features as candidates for predicting early response to chemotherapy in paediatric patients with HL. The Deauville score at iPET was useful for differentiating PFS rates.

Quantitative Dynamic 18F-FDG PET/CT in Survival Prediction of Metastatic Melanoma under PD-1 Inhibitors

Simple Summary

The reliable and early during-the-course-of-treatment assessment of tumor response to the novel immunotherapeutic agents is a matter of debate, posing relevant challenges to conventional imaging modalities. In this prospective study, including 25 metastatic melanoma patients, we explored the prognostic significance of quantitative, dynamic ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) performed early during programmed cell death protein 1 (PD-1) blockade. At a median follow-up of 24.2 months, several semiquantitative and quantitative PET/CT parameters derived from tumor lesions and reference tissues had an impact on progression-free survival (PFS). In particular, ¹⁸F-FDG standardized uptake value (SUV_mean, SUV_max) and fractal dimension (FD) of melanoma lesions adversely affected PFS, while FD of the thyroid, as well as SUV_max and k₃ of the bone marrow, positively affected PFS. These findings underline the potential predictive role of quantitative, dynamic, interim PET/CT—performed in combination with conventional, static, whole-body PET/CT—in metastatic melanoma patients under PD-1 blockade.

Abstract

The advent of novel immune checkpoint inhibitors has led to unprecedented survival rates in advanced melanoma. At the same time, it has raised relevant challenges in the interpretation of treatment response by conventional imaging approaches. In the present prospective study, we explored the predictive role of quantitative, dynamic ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) performed early during immunotherapy in metastatic melanoma patients receiving treatment with programmed cell death protein 1 (PD-1) inhibitors. Twenty-five patients under PD-1 blockade underwent dynamic and static ¹⁸F-FDG PET/CT before the start of treatment (baseline PET/CT) and after the initial two cycles of therapy (interim PET/CT). The impact of semiquantitatively (standardized uptake value, SUV) and quantitatively (based on compartment modeling and fractal analysis) derived PET/CT parameters, both from melanoma lesions and different reference tissues, on progression-free survival (PFS) was analyzed. At a median follow-up of 24.2 months, survival analysis revealed that the interim PET/CT parameters SUV_mean, SUV_max and fractal dimension (FD) of the hottest melanoma lesions adversely affected PFS, while the parameters FD of the thyroid, as well as SUV_max and k₃ of the bone marrow positively affected PFS. The herein presented findings highlight the potential predictive role of quantitative, dynamic, interim PET/CT in metastatic melanoma under PD-1 blockade. Therefore, dynamic PET/CT could be performed in selected oncological cases in combination with static, whole-body PET/CT in order to enhance the diagnostic certainty offered by conventional imaging and yield additional information regarding specific molecular and pathophysiological mechanisms involved in tumor biology and response to treatment.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event

PURPOSE

To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse large B cell lymphoma (DLBCL), and then to explore a pilot application of Neural Networks (NN) in predicting time-to-relapse.

METHODS

For conventional survival analysis, parameters as Deauville score (DS) and quantitative extension of DS (qPET) were correlated to adverse events as relapse or progression in the follow-up. To build NN and conventional multi-regression models (MM) for time-to-event prediction, patients with residual FDG uptake (DS ≥ 2) and an adverse event were divided into a training and a test group. Models developed on the training group were evaluated in the test group. Pearson correlation coefficient (R) and mean relative error between observed and forecasted time-to-event were calculated.

RESULTS

FDG-PET/CT data of 308 patients with DLBCL were analyzed. DS and qPET were prognostic factors in conventional univariate analysis. Positive and negative predictive values, respectively, were 55% and 83% for DS 4-5, 89% and 82% for positive qPET. Focusing on 37 relapsed patients with a residual FDG uptake, R between observed and forecasted time-to-event was of 0.63 in the NN model and 0.49 in the MM. Mean relative error in predicting time-to-event was of 58% for NN and 67% for MM.

CONCLUSIONS

EoT FDG-PET/CT visual score (DS) is a strong outcome predictor in DLBCL in a large monocentric cohort. The semi-quantitative parameter qPET may increase this prognostic performance. A pilot NN model applied on residual FDG uptake parameters seems to predict time-to-event in the follow-up.

Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma

Background

¹⁸F-FDG PET/CT is a standard for many B cell malignancies, while blood DNA measurements are emerging tools. Our objective was to evaluate the correlations between baseline PET parameters and circulating DNA in diffuse large B cell lymphoma (DLBCL) and classical Hodgkin lymphoma (cHL).

Methods

Twenty-seven DLBCL and forty-eight cHL were prospectively included. Twelve PET parameters were analysed. Spearman’s correlations were used to compare PET parameters each other and to circulating cell-free DNA ([cfDNA]) and circulating tumour DNA ([ctDNA]). p values were controlled by Benjamini–Hochberg correction.

Results

Among the PET parameters, three different clusters for tumour burden, fragmentation/massiveness and dispersion parameters were observed. Some PET parameters were significantly correlated with blood DNA parameters, including the total metabolic tumour surface (TMTS) describing the tumour–host interface (e.g. ρ = 0.81 p < 0.001 for [ctDNA] of DLBLC), the tumour median distance between the periphery and the centroid (medPCD) describing the tumour’s massiveness (e.g. ρ = 0.81 p < 0.001 for [ctDNA] of DLBLC) and the volume of the bounding box including tumours (TumBB) describing the disease’s dispersion (e.g. ρ = 0.83 p < 0.001 for [ctDNA] of DLBLC).

Conclusions

Some PET parameters describing tumour burden, fragmentation/massiveness and dispersion are significantly correlated with circulating DNA parameters of DLBCL and cHL patients. These results could help to understand the pathophysiology of B cell malignancies.

Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma

PURPOSE

To automatically detect lymph nodes involved in lymphoma on fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET/CT images using convolutional neural networks (CNNs).

MATERIALS AND METHODS

In this retrospective study, baseline disease of 90 patients with lymphoma was segmented on ¹⁸F-FDG PET/CT images (acquired between 2005 and 2011) by a nuclear medicine physician. An ensemble of three-dimensional patch-based, multiresolution pathway CNNs was trained using fivefold cross-validation. Performance was assessed using the true-positive rate (TPR) and number of false-positive (FP) findings. CNN performance was compared with agreement between physicians by comparing the annotations of a second nuclear medicine physician to the first reader in 20 of the patients. Patient TPR was compared using Wilcoxon signed rank tests.

RESULTS

Across all 90 patients, a range of 0-61 nodes per patient was detected. At an average of four FP findings per patient, the method achieved a TPR of 85% (923 of 1087 nodes). Performance varied widely across patients (TPR range, 33%-100%; FP range, 0-21 findings). In the 20 patients labeled by both physicians, a range of 1-49 nodes per patient was detected and labeled. The second reader identified 96% (210 of 219) of nodes with an additional 3.7 per patient compared with the first reader. In the same 20 patients, the CNN achieved a 90% (197 of 219) TPR at 3.7 FP findings per patient.

CONCLUSION

An ensemble of three-dimensional CNNs detected lymph nodes at a performance nearly comparable to differences between two physicians' annotations. This preliminary study is a first step toward automated PET/CT assessment for lymphoma.© RSNA, 2020.

Texture Analysis Improves the Value of Pretreatment 18F-FDG PET/CT in Predicting Interim Response of Primary Gastrointestinal Diffuse Large B-Cell Lymphoma

Objectives

To explore the application of pretreatment 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) texture analysis (TA) in predicting the interim response of primary gastrointestinal diffuse large B-cell lymphoma (PGIL-DLBCL).

Methods

Pretreatment 18F-FDG PET/CT images of 30 PGIL-DLBCL patients were studied retrospectively. The interim response was evaluated after 3-4 cycles of chemotherapy. The complete response (CR) rates in patients with different clinicopathological characteristics were compared by Fisher's exact test. The differences in the maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and texture features between the CR and non-CR groups were compared by the Mann-Whitney U test. Feature selection was performed according to the results of the Mann-Whitney U test and feature categories. The predictive efficacies of the SUVmax, MTV, and the selected texture features were assessed by receiver operating characteristic (ROC) analysis. A prediction probability was generated by binary logistic regression analysis.

Results

The SUVmax, MTV, some first-order texture features, volume, and entropy were significantly higher in the non-CR group. The energy was significantly lower in the non-CR group. The SUVmax, volume, and entropy were excellent predictors of the interim response, and the areas under the curves (AUCs) were 0.850, 0.805, and 0.800, respectively. The CR rate was significantly lower in patients with intestinal involvement. The prediction probability generated from the combination of the SUVmax, entropy, volume, and intestinal involvement had a higher AUC (0.915) than all single parameters.

Conclusions

TA has potential in improving the value of pretreatment PET/CT in predicting the interim response of PGIL-DLBCL. However, prospective studies with large sample sizes and validation analyses are needed to confirm the current results.

Methodological framework for radiomics applications in Hodgkin's lymphoma

BACKGROUND

According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet.

PURPOSE

The study aimed at setting up a methodological framework in radiomics applications in Hodgkin's lymphoma (HL), especially at (a) developing a novel feature selection approach, (b) evaluating radiomic intra-patient lesions' similarity, and (c) classifying relapsing refractory (R/R) vs non-(R/R) patients.

METHODS

We retrospectively included 85 patients (male:female = 52:33; median age 35 years, range 19-74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints that were tested to assess lesions' similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersampling Boosting of Tree Ensemble (RUBTE).

RESULTS

HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity).

CONCLUSIONS

Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R performed the best when all the lesions were used.

Current status and quality of radiomics studies in lymphoma: a systematic review

OBJECTIVES

To perform a systematic review regarding the developments in the field of radiomics in lymphoma. To evaluate the quality of included articles by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), the phases classification criteria for image mining studies, and the radiomics quality scoring (RQS) tool.

METHODS

We searched for eligible articles in the MEDLINE/PubMed and EMBASE databases using the terms "radiomics", "texture" and "lymphoma". The included studies were divided into two categories: diagnosis-, therapy response- and outcome-related studies. The diagnosis-related studies were evaluated using the QUADAS-2; all studies were evaluated using the phases classification criteria for image mining studies and the RQS tool by two reviewers.

RESULTS

Forty-five studies were included; thirteen papers (28.9%) focused on the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). Thirty-two (71.1%) studies were classified as discovery science according to the phase classification criteria for image mining studies. The mean RQS score of all studies was 14.2% (ranging from 0.0 to 40.3%), and 23 studies (51.1%) were given a score of < 10%.

CONCLUSION

The radiomics features could serve as diagnostic and prognostic indicators in lymphoma. However, the current conclusions should be interpreted with caution due to the suboptimal quality of the studies. In order to introduce radiomics into lymphoma clinical settings, the lesion segmentation and selection, the influence of the pathological pattern and the extraction of multiple modalities and multiple time points features need to be further studied.

KEY POINTS

• The radiomics approach may provide useful information for diagnosis, prediction of the therapy response, and outcome of lymphoma. • The quality of published radiomics studies in lymphoma has been suboptimal to date. • More studies are needed to examine lesion selection and segmentation, the influence of pathological patterns, and the extraction of multiple modalities and multiple time point features.

Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma

OBJECTIVES

To identify an ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) radiomics-based model for predicting progression-free survival (PFS) and overall survival (OS) of nasal-type extranodal natural killer/T cell lymphoma (ENKTL).

METHODS

In this retrospective study, a total of 110 ENKTL patients were divided into a training cohort (n = 82) and a validation cohort (n = 28). Forty-one features were extracted from pretreatment PET images of the patients. Least absolute shrinkage and selection operator (LASSO) regression was used to develop the radiomic signatures (R-signatures). A radiomics-based model was built and validated in the two cohorts and compared with a metabolism-based model.

RESULTS

The R-signatures were constructed with moderate predictive ability in the training and validation cohorts (R-signature_PFS: AUC = 0.788 and 0.473; R-signature_OS: AUC = 0.637 and 0.730). For PFS, the radiomics-based model showed better discrimination than the metabolism-based model in the training cohort (C-index = 0.811 vs. 0.751) but poorer discrimination in the validation cohort (C-index = 0.588 vs. 0.693). The calibration of the radiomics-based model was poorer than that of the metabolism-based model (training cohort: p = 0.415 vs. 0.428, validation cohort: p = 0.228 vs. 0.652). For OS, the performance of the radiomics-based model was poorer (training cohort: C-index = 0.818 vs. 0.828, p = 0.853 vs. 0.885; validation cohort: C-index = 0.628 vs. 0.753, p < 0.05 vs. 0.913).

CONCLUSIONS

Radiomic features derived from PET images can predict the outcomes of patients with ENKTL, but the performance of the radiomics-based model was inferior to that of the metabolism-based model.

KEY POINTS

• The R-signatures calculated by using ¹⁸F-FDG PET radiomic features can predict the survival of patients with ENKTL. • The radiomics-based models integrating the R-signatures and clinical factors achieved good predictive values. • The performance of the radiomics-based model was inferior to that of the metabolism-based model in the two cohorts.

The Complexity and Fractal Geometry of Nuclear Medicine Images

Irregularity in shape and behavior is the main feature of every anatomical system, including human organs, tissues, cells, and sub-cellular entities. It has been shown that this property cannot be quantified by means of the classical Euclidean geometry, which is only able to describe regular geometrical objects. In contrast, fractal geometry has been widely applied in several scientific fields. This rapid growth has also produced substantial insights in the biomedical imaging. Consequently, particular attention has been given to the identification of pathognomonic patterns of "shape" in anatomical entities and their changes from natural to pathological states. Despite the advantages of fractal mathematics and several studies demonstrating its applicability to oncological research, many researchers and clinicians remain unaware of its potential. Therefore, this review aims to summarize the complexity and fractal geometry of nuclear medicine images.

Baseline 18F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy

OBJECTIVES

To explore the prognostic value of positron emission tomography (PET) radiomic features in the field of diffuse large B cell lymphoma (DLBCL) treated with a first-line immunochemotherapy.

METHODS

One-hundred thirty-two patients newly diagnosed with DLBCL were retrospectively included. PET studies were reconstructed using an ordered subset expectation maximisation algorithm with point spread function modelling. The total metabolic tumour volume (MTV) was recorded for each patient, and the volume of interest structure of the largest target lesion was used to compute ¹⁸F-FDG textural parameters. Data was randomly split into training and validation datasets. Optimal cutoff values were determined by means of 2-year event-free survival (EFS) ROC analyses. Two-year EFS analyses were performed using Kaplan-Meier survival analyses and univariable and multivariable Cox regression models.

RESULTS

The median follow-up was 27 months, and the 2-year event-free survival (2y-EFS) was 77.3% in the entire population. ROC analyses for the 2y-EFS reached statistical significance for total MTV as well as four second-order metrics (homogeneity, contrast, correlation, dissimilarity) and five third-order metrics (LZE (Long-Zone Emphasis), LZLGE (Long-Zone Low-Grey Level Emphasis), LZHGE (Long-Zone High-Grey Level Emphasis), GLNU (Grey-Level Non-Uniformity) and ZP (Zone Percentage)). LZHGE displayed the highest ROC analysis accuracy (acc. = 0.76) and the best discriminant value on univariable Kaplan-Meier analysis (p < 0.0001, HR = 4.54). On multivariable analysis, including IPIaa, total MTV and LZHGE, LZHGE was the only independent predictor of 2y-EFS. These results were confirmed on the validation dataset.

CONCLUSIONS

Baseline ¹⁸F-FDG PET heterogeneity of the largest lymphoma lesion is a promising predictor of 2y-EFS in newly diagnosed DLBCL treated with immunochemotherapy.

KEY POINTS

•¹⁸F-FDG metabolic heterogeneity emerges as a new tool for survival prognostication of patients and has been explored in many solid tumours with promising results. • Baseline¹⁸F-FDG PET heterogeneity of the largest lymphoma lesion is an independent predictor of 2y-EFS in newly diagnosed DLBCL treated with immunochemotherapy. • DLBCL patients presenting with a heterogeneous tumour displayed a worse prognosis.

Predicting the pathological response to chemoradiotherapy of non-mucinous rectal cancer using pretreatment texture features based on intravoxel incoherent motion diffusion-weighted imaging

OBJECTIVES

To investigate the performance of the mean parametric values and texture features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) on identifying pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

METHODS

Pretreatment IVIM-DWI was performed on 41 LARC patients receiving nCRT in this prospective study. The values of IVIM-DWI parameters (apparent diffusion coefficient, ADC; pure diffusion coefficient, D; pseudo-diffusion coefficient, D* and perfusion fraction, f), the first-order, and gray-level co-occurrence matrix (GLCM) texture features were compared between the pCR (n = 9) and non-pathological responder (non-pCR, n = 32) groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine the efficiency for identifying pCR.

RESULTS

The values of IVIM-DWI parameters and first-order texture features did not show significant differences between the pCR and non-pCR groups. The pCR group had lower Contrast and DifVarnc values extracted from the ADC, D, and D* maps, respectively, as well as lower Correlat_D value. Higher Correlat_D*, Correlat_f, SumAverg_ADC, and SumAverg_D values were observed in the pCR group. The area under the ROC curve (AUC) values for the individual predictors in univariate analysis ranged from 0.698 to 0.837, with sensitivities from 43.75% to 87.50% and specificities from 66.67 to 100.00%. In multivariate analysis, Correlat_D* (P < 0.001), DifVarnc_ADC (P = 0.024), and DifVarnc_D (P < 0.001) were the independent predictors to pCR, with an AUC of 0.986, a sensitivity of 93.75%, and a specificity of 100.00%.

CONCLUSION

Pretreatment GLCM analysis based on IVIM-DWI may be a potential approach to identify the pathological response of LARC.

The role of PET/CT in the modern treatment of Hodgkin lymphoma

Classical Hodgkin Lymphoma is distinguished from other lymphomas by its peculiar biology and heterogeneous chemosensitivity. Most of the patients respond to the standard first-line treatment and are cured, however, in selected cases, the disease relapses or remains primarily refractory. Among predictive/prognostic factors ¹⁸FDG positron emission tomography (PET), fully integrated with computed tomography (PET/CT) proved to be extremely useful in identifying patients with poor prognosis at the time of diagnosis, during and at the end of treatment. The aim of this review is to present the current role of PET/CT in cHL at staging, interim and end of therapy assessment and its ability to guide treatment with a response- and risk-adapted strategy in clinical practice. Finally, quantitative PET measurement and the concurrent use of PET with selected biomarkers are discussed.

Evolving Role of PET-Based Novel Quantitative Techniques in the Management of Hematological Malignancies

"The role of ¹⁸F-fluorodeoxyglucose PET/computed tomography in hematological malignancies continues to expand in disease diagnosis, staging, and management. A key advantage of PET over other imaging modalities is its ability to quantify tracer uptake, which can be used to determine degree of disease activity. Although tracer uptake with PET is conventionally measured in focal lesions, novel quantitative techniques are being investigated that set objective protocols and produce robust parameters that represent total disease activity portrayed by PET. This article discusses recent advances in PET quantification that can improve reliability and accuracy of characterizing hematological malignancies."

Impact of acquisition count statistics reduction and SUV discretization on PET radiomic features in pediatric 18F-FDG-PET/MRI examinations

PURPOSE

The evaluation of features robustness with respect to acquisition and post-processing parameter changes is fundamental for the reliability of radiomics studies. The aim of this study was to investigate the sensitivity of PET radiomic features to acquisition statistics reduction and standardized-uptake-volume (SUV) discretization in PET/MRI pediatric examinations.

METHODS

Twenty-seven lesions were detected from the analysis of twenty-one 18F-FDG-PET/MRI pediatric examinations. By decreasing the count-statistics of the original list-mode data (3 MBq/kg), injected activity reduction was simulated. Two SUV discretization approaches were applied: 1) resampling lesion SUV range into fixed bins numbers (FBN); 2) rounding lesion SUV into fixed bin size (FBS). One hundred and six radiomic features were extracted. Intraclass Correlation Coefficient (ICC), Spearman correlation coefficient and coefficient-of-variation (COV) were calculated to assess feature reproducibility between low tracer activities and full tracer activity feature values.

RESULTS

More than 70% of Shape and first order features, and around 70% and 40% of textural features, when using FBS and FBN methods respectively, resulted robust till 1.2 MBk/kg. Differences in median features reproducibility (ICC) between FBS and FBN datasets were statistically significant for every activity level independently from bin number/size, with higher values for FBS. Differences in median Spearman coefficient (i.e. patient ranking according to feature values) were not statistically significant, varying the intensity resolution (i.e. bin number/size) for either FBS and FBN methods.

CONCLUSIONS

For each simulated count-statistic level, robust PET radiomic features were determined for pediatric PET/MRI examinations. A larger number of robust features were detected when using FBS methods.

A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma

First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUV_max). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.

Metabolic Tumor Volume Metrics in Lymphoma

Although visual assessment using the Deauville criteria is strongly recommended by guidelines for treatment response monitoring in all FDG-avid lymphoma histologies, the high rate of false-positives and concerns about interobserver variability have motivated the development of quantitative tools to facilitate objective measurement of tumor response in both routine and clinical trial settings. Imaging studies using functional quantitative measures play a significant role in profiling oncologic processes. These quantitative metrics allow for objective end points in multicenter clinical trials. However, the standardization of imaging procedures including image acquisition parameters, reconstruction and analytic measures, and validation of these methods are essential to enable an individualized treatment approach. A robust quality control program associated with the inclusion of proper scanner calibration, cross-calibration with dose calibrators and across other scanners is required for accurate quantitative measurements. In this section, we will review the technical and methodological considerations related to PET-derived quantitative metrics and the relevant published data to emphasize the potential value of these metrics in the prediction of patient prognosis in lymphoma.

Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging

The aim of the study was to investigative the utility of gray-level co-occurrence matrix (GLCM) texture analysis based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the early response to chemoradiotherapy for nasopharyngeal carcinoma (NPC).Baseline IVIM-DWI was performed on 81 patients with NPC receiving chemoradiotherapy in a prospective nested case-control study. The patients were categorized into the residue (n = 11) and nonresidue (n = 70) groups, according to whether there was local residual lesion or not at the end of chemoradiotherapy. The pretreatment tumor volume and the values of IVIM-DWI parameters (apparent diffusion coefficient [ADC], D, D, and f) and GLCM features based on IVIM-DWI were compared between the 2 groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine significant indicator of treatment response.The nonresidue group had lower tumor volume, ADC, D, CorrelatADC, CorrelatD, InvDfMomADC, InvDfMomD and InvDfMomD values, together with higher ContrastD, Contrastf, SumAvergADC, SumAvergD, and SumAvergD values, than the residue group (all P < .05). Based on ROC curve in univariate analysis, the area under the curve (AUC) values for individual GLCM features in the prediction of the treatment response ranged from 0.635 to 0.879, with sensitivities from 54.55% to 100.00% and specificities from 52.86% to 85.71%. Multivariate logistic regression analysis demonstrated D (P = .026), InvDfMomADC (P = .033) and SumAvergD (P = .015) as the independent predictors for identifying NPC without residue, with an AUC value of 0.977, a sensitivity of 90.91% and a specificity of 95.71%.Pretreatment GLCM features based on IVIM-DWI, especially on the diffusion-related maps, may have the potential to predict the early response to chemoradiotherapy for NPC.

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving ¹⁸F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

Pretherapeutic FDG-PET total metabolic tumor volume predicts response to induction therapy in pediatric Hodgkin's lymphoma

Background

Standardized treatment in pediatric patients with Hodgkin’s lymphoma (HL) follows risk stratification by tumor stage, erythrocyte sedimentation rate and tumor bulk. We aimed to identify quantitative parameters from pretherapeutic FDG-PET to assist prediction of response to induction chemotherapy.

Methods

Retrospective analysis in 50 children with HL (f:18; m:32; median age, 14.8 [4–18] a) consecutively treated according to EuroNet-PHL-C1 (n = 42) or -C2 treatment protocol (n = 8). Total metabolic tumor volume (MTV) in pretherapeutic FDG-PET was defined using a semi-automated, background-adapted threshold. Metabolic (SUVmax, SUVmean, SUVpeak, total lesion glycolysis [MTV*SUVmean]) and heterogeneity parameters (asphericity [ASP], entropy, contrast, local homogeneity, energy, and cumulative SUV-volume histograms) were derived. Early response assessment (ERA) was performed after 2 cycles of induction chemotherapy according to treatment protocol and verified by reference rating. Prediction of inadequate response (IR) in ERA was based on ROC analysis separated by stage I/II (1 and 26 patients) and stage III/IV disease (7 and 16 patients) or treatment group/level (TG/TL) 1 to 3.

Results

IR was seen in 28/50 patients (TG/TL 1, 6/12 patients; TG/TL 2, 10/17; TG/TL 3, 12/21). Among all PET parameters, MTV best predicted IR; ASP was the best heterogeneity parameter. AUC of MTV was 0.84 (95%-confidence interval, 0.69–0.99) in stage I/II and 0.86 (0.7–1.0) in stage III/IV. In patients of TG/TL 1, AUC of MTV was 0.92 (0.74–1.0); in TG/TL 2 0.71 (0.44–0.99), and in TG/TL 3 0.85 (0.69–1.0). Patients with high vs. low MTV had IR in 86 vs. 0% in TG/TL 1, 80 vs. 29% in TG/TL 2, and 90 vs. 27% in TG/TL 3 (cut-off, > 80 ml, > 160 ml, > 410 ml).

Conclusions

In this explorative study, high total MTV best predicted inadequate response to induction therapy in pediatric HL of all pretherapeutic FDG-PET parameters – in both low and high stages as well as the 3 different TG/TL.

Trial registration

Ethics committee number: EA2/151/16 (retrospectively registered).

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4432-4) contains supplementary material, which is available to authorized users.

Tumor fragmentation estimated by volume surface ratio of tumors measured on 18F-FDG PET/CT is an independent prognostic factor of diffuse large B-cell lymphoma

INTRODUCTION

Our aim was to study the prognostic value of two new ¹⁸F-FDG PET biomarkers in diffuse large B-cell lymphoma (DLBCL). We examined the total tumor surface (TTS), describing the tumor-host interface, and the tumor volume surface ratio (TVSR), corresponding to the ratio between the total metabolic tumor volume (TMTV) and TTS, describing the tumor fragmentation.

METHODS

We retrospectively included 215 patients with DLBCL. Patients underwent initial ¹⁸F-FDG PET/CT before R-CHOP (73%) or intensified R-CHOP (R-ACVBP) regimens (27%). The TMTV was measured using a fixed threshold value of 41% of SUVmax. To calculate TTS and TVSR, the surface was measured using an in-house software based on the marching cube algorithm. Spearman's rank correlation coefficient (ρ) was computed between TMTV, TTS, and TVSR, and ROC analysis was performed. Survival functions at 5 years were studied using a Kaplan-Meier method and uni/multivariate Cox analysis.

RESULTS

TVSR was poorly correlated with TMTV (ρ = 0.5) and TTS (ρ = 0.26), while TTS was highly correlated with TMTV (ρ = 0.94) and was, therefore, excluded from the analysis. TMTV had the highest area under the ROC curve (0.711) and the best sensitivity (0.797), while TVSR had the best specificity (0.745). The optimal cut-off values to predict 5-year OS were 222 cm³ for TMTV and 6.0 mm for TVSR. Patients with high TMTV and TVSR had significantly worse prognosis in Kaplan-Meier and Cox univariate analysis. In a multivariate Cox analysis combining the International Prognostic Index (IPI), the type of chemotherapy, TMTV, and TVSR, all parameters were independent and significant prognostic factors (HR [95%CI]: IPI 1.4 [1.1-1.8], type of chemotherapy 4.5 [2.0-10.5], TMTV 2.8 [1.4-5.5], TVSR 2.1 [1.3-3.4]). A synergistic effect between TMTV and TVSR was observed in a Kaplan-Meier analysis combining the two parameters.

CONCLUSIONS

TVSR measured on the initial ¹⁸F-FDG PET is an independent prognostic factor in DLBCL and has an additional prognostic value when combined with TMTV, IPI score and chemotherapy.

PET-CT evaluation of the curative effect of crizotinib on malignant myofibroblastoma with rare mutation of ALK R401: a case report and literature review

Objective

The purpose of this article is to explore the targeted treatment of malignant myofibroblastoma and evaluate the role of neoplasm metabolite markers in the evaluation of efficacy after targeted therapy.

Method

This report described a case of myofibroblastic sarcoma with rare mutation of ALK R401 in a 58-year-old man prescribed with crizotinib, to evaluate its curative effect by positron emission tomography coupled with computed tomography (PET-CT). After the progressive disease in the brain, bevacizumab combined with crizotinib was administered. The Response Evaluation Criteria in Solid Tumors (RECIST), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were used to assess the efficacy. The efficacy was assessed by comparing changes in MTV and TLG.

Result

After the treatment of crizotinib, the tumor volume was decreased. However, bevacizumab combined with crizotinib had not improved the prognosis. The change of MTV and TLG was consistent with the efficacy. The increase of MTV and TLG is an early indicator of the poor prognosis of patients.

Conclusion

The treatment of the crizotinib patient with the mutation of ALK R401 was effective. The values of MTV and TLG reflected the prognosis earlier than RECIST.

Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods

Background

Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification.

Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated.

Results

Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group.

Conclusion

Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0379-3) contains supplementary material, which is available to authorized users.