Role of Radiomics-Based Baseline PET/CT Imaging in Lymphoma: Diagnosis, Prognosis, and Response Assessment

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients.

Application effect of 18F-FDG PET/CT technique in diagnosis and prognosis evaluation of lymphoma

The objective of the study was to research diagnostic and prognostic values of 18F fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) in patients with diffuse large B-cell lymphoma (DLBCL). The diagnostic sensitivity (Sen) of PET/CT (94.75 %) was remarkably higher than 83.56 % of B-US. Age ≥ 65 years old, maximum focal diameter ≥5 cm, clinical stages III-IV, systemic symptoms, increased lactate dehydrogenase level, high modified international prognostic index score, Ecog score ≥1, B-cell lymphoma 2 (Bcl-2) gene, MYC protein expression rate, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were all factors that influenced the recurrence or progression of DLBCL. With higher MTV and TLG, patients would have a greater probability of recurrence or progression. 18F-FDG PET/CT showed a high diagnostic Sen in lymphoma lesions, and could accurately guide clinical staging. Combined with clinical parameters, laboratory indicators, and metabolic parameters, prognostic indicators of patients could be evaluated more accurately.

Radiomics incorporating deep features for predicting Parkinson's disease in 123I-Ioflupane SPECT

PURPOSE

123I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson's disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using 123I-Ioflupane SPECT images at year 0.

METHODS

In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and 123I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models.

RESULTS

For the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models.

CONCLUSION

The combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using 123I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features.

The Diagnostic Performance of 2-[18F]FDG PET/CT in Identifying Richter Transformation in Chronic Lymphocytic Leukemia: An Updated Systematic Review and Bivariate Meta-Analysis

Richter transformation is a rare phenomenon characterized by the transformation of cell chronic lymphocytic leukemia (CLL) into a more aggressive lymphoma variant. The early identification of CLLs with a high risk of RT is fundamental. In this field, 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) has been shown to be a non-invasive and promising tool, but apparently, unclear data seem to be present in the literature. This systematic review and bivariate meta-analysis aimed to investigate the diagnostic performance of 2-[18F]FDG PET/CT and its parameters in predicting RT. Between 2006 and 2024, 15 studies were published on this topic, including 1593 CLL patients. Among semiquantitative variables, SUVmax was the most investigated, and the best threshold derived for detecting RT was five. With this cut-off value, a pooled sensitivity of 86.8% (95% CI: 78.5-93.3), a pooled specificity of 48.1% (95% CI: 27-69.9), a pooled negative predictive value of 90.5% (95% CI: 88.4-92.4), a pooled negative likelihood ratio of 0.35 (95% CI: 0.17-0.70), a pooled positive likelihood ratio of 1.8 (95% CI: 1.3-2.4), and a pooled diagnostic odds ratio of 6.7 (3.5-12.5) were obtained. With a higher cut-off (SUVmax = 10), the specificity increased while the sensitivity reduced. The other metabolic features, like metabolic tumor volume, total lesion glycolysis, and radiomic features, were only marginally investigated with controversial evidence.

Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging

PURPOSE

To explore the clinical value of a clinical radiomics model nomogram based on magnetic resonance imaging (MRI) for preoperative meningioma grading.

MATERIALS AND METHODS

We collected retrospectively 544 patients with pathological diagnosis of meningiomas were categorized into training (n = 380) and validation (n = 164) groups at the ratio of 7∶ 3. There were 3,376 radiomics features extracted from T2WI and T1C by shukun technology platform after manual segmentation using an independent blind method by two radiologists. The Selectpercentile and Lasso are used to filter the most strongly correlated features. Random forest (RF) radiomics model and clinical radiomics model nomogram were constructed respectively. The calibration, discrimination, and clinical validity were evaluated by using the calibration curve and decision analysis curve (DCA).

RESULTS

The RF radiomics model based on T1C and T2WI was the most effective to predict meningioma grade before surgery among the six different classifiers. The predictive ability of clinical radiomics model was slightly higher than that of RF model alone. The AUC, SEN, SPE, and ACC of the training set were 0.949, 0.976, 0.785, and 0.826, and the AUC, SEN, SPE, and ACC of the validation set were 0.838, 0.829, 0.783, and 0.793, respectively. The calibration curve and Hosmer-Lemeshow test showed the predictive probability of the fusion model was similar to the actual differentiated LGM and HGM. The analysis of the decision curve showed that the clinical radiomics model could obtain the best clinical net profit.

CONCLUSIONS

The clinical radiomics model nomogram based on T1C and T2WI has high accuracy and sensitivity for predicting meningioma grade.

Typical and Atypical Imaging Features of Malignant Lymphoma in the Abdomen and Mimicking Diseases

Malignant lymphoma typically presents with homogeneous enhancement of enlarged lymph nodes without internal necrotic or cystic changes on multiphasic CT, which can be suspected without invasive diagnostic methods. However, some subtypes of malignant lymphoma show atypical imaging features, which makes diagnosis challenging for radiologists. Moreover, there are several lymphoma-mimicking diseases in current clinical practice, including leukemia, viral infections in immunocompromised patients, and primary or metastatic cancer. The ability of diagnostic processes to distinguish malignant lymphoma from mimicking diseases is necessary to establish effective management strategies for initial radiological examinations. Therefore, this study aimed to discuss the typical and atypical imaging features of malignant lymphoma as well as mimicking diseases and discuss important diagnostic clues that can help narrow down the differential diagnosis.

Axial Skeleton Radiomics of 18F-FDG PET/CT: Impact on Event-Free Survival Prediction in High-Risk Pediatric Neuroblastoma

OBJECTIVES

To construct and validate a combined model based on axial skeleton radiomics of 18F-FDG PET/CT for predicting event-free survival in high-risk pediatric neuroblastoma patients.

MATERIALS AND METHODS

Eighty-seven high-risk neuroblastoma patients were retrospectively enrolled in this study and randomized in a 7:3 ratio to the training and validation cohorts. The radiomics model was constructed using radiomics features that were extracted from the axial skeleton. A univariate Cox regression analysis was then performed to screen clinical risk factors associated with event-free survival for building clinical model. Radiomics features and clinical risk factors were incorporated to construct the combined model for predicting the event-free survival in high-risk neuroblastoma patients. The performance of the models was evaluated by the C-index.

RESULTS

Eighteen radiomics features were selected to build the radiomics model. The radiomics model achieved better event-free survival prediction than the clinical model in the training cohort (C-index: 0.846 vs. 0.612) and validation cohort (C-index: 0.754 vs. 0.579). The combined model achieved the best prognostic prediction performance with a C-index of 0.863 and 0.799 in the training and validation cohorts, respectively.

CONCLUSION

The combined model integrating radiomics features and clinical risk factors showed more accurate predictive performance for event-free survival in high-risk pediatric neuroblastoma patients, which helps to design individualized treatment strategies and regular follow-ups.

Predictive value of 18F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation

PURPOSE

To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods.

METHODS

One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test.

RESULTS

A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test.

CONCLUSION

It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.

Assessment of Response to Immunotherapy in Patients with Hodgkin Lymphoma: Towards Quantifying Changes in Tumor Burden Using FDG-PET/CT

Immune checkpoint inhibitors are currently the standard of care for many advanced solid tumors, and they have been recently approved for the treatment of relapsed/refractory Hodgkin lymphoma and primary mediastinal B cell lymphoma. Assessments of the response to immunotherapy may be complicated by the occurrence of the flare/pseudoprogression phenomenon, consisting of initial tumor enlargement and even the appearance of new lesions, followed by a response, which may initially be indistinguishable from true progression. There have been efforts to characterize and capture the new patterns of response observed during immunotherapy, namely, pseudoprogression and delayed response, and several immune-related response criteria have been proposed. Confirming progression on a subsequent scan and measuring the total tumor burden are both common in immune-related criteria. Due to the peculiarity of hematologic malignancies, lymphoma-specific immune-related criteria have been developed (LYRIC), and they have been evaluated in research studies in comparison to the Lugano Classification. In this review work, we illustrate the evolution of the response criteria in lymphomas from the first CT-based criteria to the development of the PET-based Lugano Classification, further refined to take into account the flare phenomenon encountered during immunotherapy. We also describe the additional contribution of PET-derived volumetric parameters to the interpretation of responses during immunotherapy.

PET/CT in Non-Hodgkin Lymphoma: An Update

Non-Hodgkin lymphomas represents a heterogeneous group of lymphoproliferative disorders characterized by different clinical courses, varying from indolent to highly aggressive. ¹⁸F-FDG-PET/CT is the current state-of-the-art diagnostic imaging, for the staging, restaging and evaluation of response to treatment in lymphomas with avidity for ¹⁸F-FDG, despite it is not routinely recommended for surveillance. PET-based response criteria (using five-point Deauville Score) are nowadays uniformly applied in FDG-avid lymphomas. In this review, a comprehensive overview of the role of ¹⁸F-FDG-PET in Non-Hodgkin lymphomas is provided, at each relevant point of patient management, particularly focusing on recent advances on diffuse large B-cell lymphoma and follicular lymphoma, with brief updates also on other histotypes (such as marginal zone, mantle cell, primary mediastinal- B cell lymphoma and T cell lymphoma). PET-derived semiquantitative factors useful for patient stratification and prognostication and emerging radiomics research are also presented.

18F-FDG PET/CT Maximum Tumor Dissemination (Dmax) in Lymphoma: A New Prognostic Factor?

Recently, several studies introduced the potential prognostic usefulness of maximum tumor dissemination (Dmax) measured by 2-deoxy-2-fluorine-18-fluoro-D-glucose positron-emission tomography/computed tomography (¹⁸F-FDG PET/CT). Dmax is a simple three-dimensional feature that represents the maximal distance between the two farthest hypermetabolic PET lesions. A comprehensive computer literature search of PubMed/MEDLINE, Embase, and Cochrane libraries was conducted, including articles indexed up to 28 February 2023. Ultimately, 19 studies analyzing the value of ¹⁸F-FDG PET/CT Dmax in patients with lymphomas were included. Despite their heterogeneity, most studies showed a significant prognostic role of Dmax in predicting progression-free survival (PFS) and overall survival (OS). Some articles showed that the combination of Dmax with other metabolic features, such as MTV and interim PET response, proved to better stratify the risk of relapse or death. However, some methodological open questions need to be clarified before introducing Dmax into clinical practice.

Clinical value of 18F-FDG PET/CT in the management of HIV-associated lymphoma

HIV is still a major public health problem. At present, HIV-associated lymphoma remains the leading cause of deaths among people living with HIV, which should be paid more attention to. ¹⁸F-fluorodeoxglucose (FDG) PET/CT has been recommended in the initial staging, restaging, response assessment and prognostic prediction of lymphomas in general population. HIV-associated lymphoma is, however, a different entity from lymphoma in HIV-negative with a poorer prognosis. The ability to accurately risk-stratify HIV-infected patients with lymphoma will help guide treatment strategy and improve the prognosis. In the review, the current clinical applications of ¹⁸F-FDG PET/CT in HIV-associated lymphoma will be discussed, such as diagnosis, initial staging, response evaluation, prognostic prediction, PET-guided radiotherapy decision, and surveillance for recurrence. Moreover, future perspectives will also be presented.

PET-CT in Clinical Adult Oncology: I. Hematologic Malignancies

PET-CT is an advanced imaging modality with many oncologic applications, including staging, assessment of response to therapy, restaging and evaluation of suspected recurrence. The goal of this 6-part series of review articles is to provide practical information to providers and imaging professionals regarding the best use of PET-CT for the more common adult malignancies. In the first article of this series, hematologic malignancies are addressed. The classification of these malignancies will be outlined, with the disclaimer that the classification of lymphomas is constantly evolving. Critical applications, potential pitfalls, and nuances of PET-CT imaging in hematologic malignancies and imaging features of the major categories of these tumors are addressed. Issues of clinical importance that must be reported by the imaging professionals are outlined. The focus of this article is on [¹⁸F] fluorodeoxyglucose (FDG), rather that research tracers or those requiring a local cyclotron. This information will serve as a resource for the appropriate role and limitations of PET-CT in the clinical management of patients with hematological malignancy for health care professionals caring for adult patients with hematologic malignancies. It also serves as a practical guide for imaging providers, including radiologists, nuclear medicine physicians and their trainees.

CT radiomics to predict Deauville score 4 positive and negative Hodgkin lymphoma manifestations

18F-FDG-PET/CT is standard to assess response in Hodgkin lymphoma by quantifying metabolic activity with the Deauville score. PET/CT, however, is time-consuming, cost-extensive, linked to high radiation and has a low availability. As an alternative, we investigated radiomics from non-contrast-enhanced computed tomography (NECT) scans. 75 PET/CT examinations of 43 patients on two different scanners were included. Target lesions were classified as Deauville score 4 positive (DS4+) or negative (DS4-) based on their SUVpeak and then segmented in NECT images. From these segmentations, 107 features were extracted with PyRadiomics. All further statistical analyses were then performed scanner-wise: differences between DS4+ and DS4- manifestations were assessed with the Mann-Whitney-U-test and single feature performances with the ROC-analysis. To further verify the reliability of the results, the number of features was reduced using different techniques. The feature median showed a high sensitivity for DS4+ manifestations on both scanners (scanner A: 0.91, scanner B: 0.85). It furthermore was the only feature that remained in both datasets after applying different feature reduction techniques. The feature median from NECT concordantly has a high sensitivity for DS4+ Hodgkin manifestations on two different scanners and thus could provide a surrogate for increased metabolic activity in PET/CT.

Follicular lymphoma without lymphadenopathy incidentally diagnosed by sentinel lymph node biopsy during breast cancer surgery: a case report

Background

Concurrent breast cancer and malignant lymphoma is a rare phenomenon. This report describes malignant lymphoma that was incidentally diagnosed from a sentinel lymph node biopsy (SLNB) during breast cancer surgery.

Case presentation

A 73-year-old woman with a history of ovarian cancer and diabetes presented with right focal asymmetric density on a mammogram acquired during routine breast cancer screening. Ultrasonography (US) and magnetic resonance imaging (MRI) showed a 13.5-mm tumor in the upper lateral region of the right breast. A US-guided Mammotome biopsy revealed invasive ductal carcinoma of the right breast. Preoperative assessments including positron emission tomography–computerized tomography, found no evidence of axillary lymphadenopathy or distant metastasis. Because the breast cancer was stage I, the patient underwent a right mastectomy and a sentinel lymph node biopsy (SLNB) at our hospital. Pathological assessment of the biopsy revealed follicular lymphoma (FL), but no metastatic breast cancer. The patient was referred to hematology to stage the FL. Bone marrow findings were negative and stage I FL was diagnosed. After the mastectomy, she was monitored and given adjuvant therapy with an aromatase inhibitor.

Conclusions

Follicular lymphoma was incidentally diagnosed from an SLNB obtained to determine the dissemination of early-stage breast cancer. Collaboration with hematologists is important to determine optimal treatment plans for such patients regardless of the rarity of such events.