Emerging and Evolving Concepts in Cancer Immunotherapy Imaging

The emerging role of AI in enhancing intratumoral immunotherapy care

The emergence of immunotherapy (IO), and more recently intratumoral IO presents a novel approach to cancer treatment which can enhance immune responses while allowing combination therapy and reducing systemic adverse events. These techniques are intended to change the therapeutic paradigm of oncology care, and means that traditional assessment methods are inadequate, underlining the importance of adopting innovative approaches. Artificial intelligence (AI) with machine learning algorithms and radiomics are promising approaches, offering new insights into patient care by analyzing complex imaging data to identify biomarkers to refine diagnosis, guide interventions, predict treatment responses, and adapt therapeutic strategies. In this editorial, we explore how integrating these technologies could revolutionize personalized oncology. We discuss their potential to enhance the survival and quality of life of patients treated with intratumoral IO by improving treatment effectiveness and minimizing side effects, potentially reshaping practice guidelines. We also identify areas for future research and review clinical trials to confirm the efficacy of these promising approaches.

Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer

Purpose

This study aimed to establish and evaluate the value of integrated models involving 18F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC).

Methods

A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance.

Results

The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application.

Conclusions

The 18F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.

The role of CD8 PET imaging in guiding cancer immunotherapy

Currently, immunotherapy is being widely used for treating cancers. However, the significant heterogeneity in patient responses is a major challenge for its successful application. CD8-positive T cells (CD8+ T cells) play a critical role in immunotherapy. Both their infiltration and functional status in tumors contribute to treatment outcomes. Therefore, accurate monitoring of CD8+ T cells, a potential biomarker, may improve therapeutic strategy. Positron emission tomography (PET) is an optimal option which can provide molecular imaging with enhanced specificity. This review summarizes the mechanism of action of CD8+ T cells in immunotherapy, and highlights the recent advancements in PET-based tracers that can visualize CD8+ T cells and discusses their clinical applications to elucidate their potential role in cancer immunotherapy.

A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma

This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.

A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma

Background

Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced computed tomography (CT) and combined with clinical data to predict the major pathological response to NIT in ESCC patients.

Methods

This retrospective study included 82 ESCC patients who were randomly divided into the training group (n = 57) and the validation group (n = 25). Radiomic features were derived from the tumor region in enhanced CT images obtained before treatment. After feature reduction and screening, radiomics was established. Logistic regression analysis was conducted to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. Area under curve (AUC) was applied to evaluate the predictive ability of the models, and decision curve analysis (DCA) and calibration curves were performed to test the application of the models.

Results

One clinical data (radiotherapy) and 10 radiomic features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve excellent predictive performance, with AUC values of 0.93 (95% CI 0.87-0.99) and 0.85 (95% CI 0.69-1.00) in the training group and the validation group, respectively. DCA and calibration curves demonstrated a good clinical feasibility and utility of this model.

Conclusion

Enhanced CT image-based radiomics could predict the response of ESCC patients to NIT with high accuracy and robustness. The developed predictive model offers a valuable tool for assessing treatment efficacy prior to initiating therapy, thus providing individualized treatment regimens for patients.

Cancer Immunotherapy and Medical Imaging Research Trends from 2003 to 2023: A Bibliometric Analysis

Purpose

With the rapid development of immunotherapy, cancer treatment has entered a new phase. Medical imaging, as a primary diagnostic method, is closely related to cancer immunotherapy. However, until now, there has been no systematic bibliometric analysis of the state of this field. Therefore, the main purpose of this article is to clarify the past research trajectory, summarize current research hotspots, reveal dynamic scientific developments, and explore future research directions.

Patients and Methods

A comprehensive search was conducted on the Web of Science Core Collection (WoSCC) database to identify publications related to immunotherapy specifically for the medical imaging of carcinoma. The search spanned the period from the year 2003 to 2023. Several analytical tools were employed. These included CiteSpace (6.2.4), and the Microsoft Office Excel (2016).

Results

By searching the database, a total of 704 English articles published between 2003 and 2023 were obtained. We have observed a rapid increase in the number of publications since 2018. The two most active countries are the United States (n=265) and China (n=170). Pittock, Sean J and Abu-sbeih, Hamzah are very concerned about the relationship between cancer immunotherapy and medical images and have published more academic papers (n = 5; n = 4). Among the top 10 co-cited authors, Topalian Sl (n=43) cited ranked first, followed by Graus F (n=40) cited. According to clustering, timeline, and burst word analysis, the results show that the current research focus is on "MRI", "deep learning", "tumor microenvironment" and so on.

Conclusion

Medical imaging and cancer immunotherapy are hot topics. The United States is the country with the most publications and the greatest influence in this field, followed by China. "MRI", "PET/PET-CT", "deep learning", "immune-related adverse events" and "tumor microenvironment" are currently hot research topics and potential targets.

Radiological assessment of immunotherapy effects and immune checkpoint-related pneumonitis for lung cancer

Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life-threatening immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI-pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X-ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI-pneumonitis. Additionally, it is tried to differentiate ICI-pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID-19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help.

Noninvasive radiomic biomarkers for predicting pseudoprogression and hyperprogression in patients with non-small cell lung cancer treated with immune checkpoint inhibition

This study aimed to develop a computed tomography (CT)-based radiomics model capable of precisely predicting hyperprogression and pseudoprogression (PP) in patients with non-small cell lung cancer (NSCLC) treated with immunotherapy. We retrospectively analyzed 105 patients with NSCLC, from three institutions, treated with immune checkpoint inhibitors (ICIs) and categorized them into training and independent testing set. Subsequently, we processed CT scans with a series of image-preprocessing techniques, and 6008 radiomic features capturing intra- and peritumoral texture patterns were extracted. We used the least absolute shrinkage and selection operator logistic regression model to select radiomic features and construct machine learning models. To further differentiate between progressive disease (PD) and hyperprogressive disease (HPD), we developed a new radiomics model. The logistic regression (LR) model showed optimal performance in distinguishing PP from HPD, with areas under the receiver operating characteristic curve (AUC) of 0.95 (95% confidence interval [CI]: 0.91-0.99) and 0.88 (95% CI: 0.66-1) in the training and testing sets, respectively. Additionally, the support vector machine model showed optimal performance in distinguishing PD from HPD, with AUC of 0.97 (95% CI: 0.93-1) and 0.87 (95% CI: 0.72-1) in the training and testing sets, respectively. Kaplan‒Meier survival curves showed clear stratification between PP predicted by the radiomics model and true progression (HPD and PD) (hazard ratio = 0.337, 95% CI: 0.200-0.568, p < 0.01) in overall survival. Our study demonstrates that radiomic features extracted from baseline CT scans are effective in predicting PP and HPD in patients with NSCLC treated with ICIs.

The Prognostic Role of [18F]FDG PET/CT in Patients with Advanced Cutaneous Squamous Cell Carcinoma Submitted to Cemiplimab Immunotherapy: A Single-Center Retrospective Study

Background: Baseline 2-deoxy-2[18F]fluoro-d-glucose ([18F]FDG) positron emission tomography (PET)-derived parameters and 12-week metabolic response were investigated as prognostic factors in advanced cutaneous squamous cell carcinoma (cSCC) submitted to cemiplimab immunotherapy. Materials and Methods: Clinical records of 25 cSCC patients receiving cemiplimab, submitted to [18F]FDG positron emission tomography/computed tomography (PET/CT) at baseline and after ∼12 weeks, were retrospectively reviewed. The Kaplan-Meier (KM) method was applied to analyze differences in event-free survival (EFS), and Cox regression analysis was employed to identify the prognostic factors. Results: At the 12-week PET/CT evaluation, 16 patients (64%) were classified as responders (complete or partial response) and 9 (36%) as nonresponders ("unconfirmed progressive metabolic disease") according to immune PET Response Criteria in Solid Tumors (iPERCIST). By KM analysis, baseline metabolic tumor volume (MTV) and total lesion glycolysis (TLG) significantly correlated with the EFS (p < 0.05). Furthermore, the KM analysis showed that the lack of metabolic response at 12 weeks was associated with meaningfully shorter EFS (7.2 ± 1 months in nonresponders vs. 20.3 ± 2.3 months in responders). In Cox multivariate analysis, metabolic response at 12 weeks remained the only predictor of the EFS (p < 0.05). Conclusions: Baseline tumor load (i.e., MTV and TLG) and metabolic response at 12 weeks may have a prognostic impact in cSCC patients treated with cemiplimab.

Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study

Immunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge. We analyzed the association between several radiomics features and response to immunotherapy in 53 patients treated with checkpoint inhibitors for advanced renal cell carcinoma. We found that the following features are associated with progression of disease as best tumor response: F_stat.range (p < .0004), F_stat.max (p < .0007), F_stat.var (p < .0016), F_stat.uniformity (p < .0020), F_stat.90thpercentile (p < .0050). Gross tumor volumes characterized by high values of F_stat.var and F_stat.max (greater than 60,000 and greater than 300, respectively) are most likely related to a high risk of progression. Further analyses are warranted to confirm these results. Radiomics, together with other potential predictive factors, such as gut microbiota, genetic features or circulating immune molecules, could allow a personalized treatment for patients with advanced renal cell carcinoma.

Biomarkers for immunotherapy of hepatocellular carcinoma

Immune-checkpoint inhibitors (ICIs) are now widely used for the treatment of patients with advanced-stage hepatocellular carcinoma (HCC). Two different ICI-containing regimens, atezolizumab plus bevacizumab and tremelimumab plus durvalumab, are now approved standard-of-care first-line therapies in this setting. However, and despite substantial improvements in survival outcomes relative to sorafenib, most patients with advanced-stage HCC do not derive durable benefit from these regimens. Advances in genome sequencing including the use of single-cell RNA sequencing (both of tumour material and blood samples), as well as immune cell identification strategies and other techniques such as radiomics and analysis of the microbiota, have created considerable potential for the identification of novel predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from ICIs. In this Review, we summarize data on the immunology of HCC and the outcomes in patients receiving ICIs for the treatment of this disease. We then provide an overview of current biomarker use and developments in the past 5 years, including gene signatures, circulating tumour cells, high-dimensional flow cytometry, single-cell RNA sequencing as well as approaches involving the microbiome, radiomics and clinical markers. Novel concepts for further biomarker development in HCC are then discussed including biomarker-driven trials, spatial transcriptomics and integrated 'big data' analysis approaches. These concepts all have the potential to better identify patients who are most likely to benefit from ICIs and to promote the development of new treatment approaches.

The radiomic biomarker in non-small cell lung cancer: 18F-FDG PET/CT characterisation of programmed death-ligand 1 status

AIM

To present an integrated 2-[18F]-fluoro-2-deoxy-d-glucose (18F-FDG) positron-emission tomography (PET)/computed tomography (CT) radiomic characterisation of programmed death-ligand 1 (PD-L1) status in non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

In this retrospective study, 18F-FDG PET/CT images and clinical data of 394 eligible patients were divided into training (n=275) and test sets (n=119). Next, the corresponding nodule of interest was segmented manually on the axial CT images by radiologists. After which, the spatial position matching method was used to match the image positions of CT and PET, and radiomic features of the CT and PET images were extracted. Radiomic models were built using five different machine-learning classifiers and the performance of the radiomic models were further evaluated. Finally, a radiomic signature was established to predict the PD-L1 status in patients with NSCLC using the features in the best performing radiomic model.

RESULTS

The radiomic model based on the PET intranodular region determined using the logistic regression classifier preformed best, yielding an area under the receiver operating characteristics curve (AUC) of 0.813 (95% CI: 0.812, 0.821) on the test set. The clinical features did not improve the test set AUC (0.806, 95% CI: 0.801, 0.810). The final radiomic signature for PD-L1 status was consisted of three PET radiomic features.

CONCLUSION

This study showed that an 18F-FDG PET/CT-based radiomic signature could be used as a non-invasive biomarker to discriminate PD-L1-positive from PD-L1-negative in patients with NSCLC.

Added Value of FDG PET/MRI in Gynecologic Oncology: A Pictorial Review

Fluorine 18-fluorodeoxyglucose (FDG) PET and MRI independently play a valuable role in the management of patients with gynecologic malignancies, particularly endometrial and cervical cancer. The PET/MRI hybrid imaging technique combines the metabolic information obtained from PET with the excellent soft-tissue resolution and anatomic details provided by MRI in a single examination. MRI is the modality of choice for assessment of local tumor extent in the pelvis, whereas PET is used to assess for local-regional spread and distant metastases. The authors discuss the added value of FDG PET/MRI in imaging gynecologic malignancies of the pelvis, with a focus on the role of FDG PET/MRI in diagnosis, staging, assessing treatment response, and characterizing complications. PET/MRI allows better localization and demarcation of the extent of disease, characterization of lesions and involvement of adjacent organs and lymph nodes, and improved differentiation of benign from malignant tissues, as well as detection of the presence of distant metastasis. It also has the advantages of decreased radiation dose and a higher signal-to-noise ratio of a prolonged PET examination of the pelvis contemporaneous with MRI. The authors provide a brief technical overview of PET/MRI, highlight how simultaneously performed PET/MRI can improve stand-alone MRI and PET/CT in gynecologic malignancies, provide an image-rich review to illustrate practical and clinically relevant applications of this imaging technique, and review common pitfalls encountered in clinical practice. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Short-term immune-checkpoint inhibition partially rescues perturbed bone marrow hematopoiesis in mismatch-repair deficient tumors

Wide-spread cancer-related immunosuppression often curtails immune-mediated antitumoral responses. Immune-checkpoint inhibitors (ICIs) have become a state-of-the-art treatment modality for mismatch repair-deficient (dMMR) tumors. Still, the impact of ICI-treatment on bone marrow perturbations is largely unknown. Using anti-PD1 and anti-LAG-3 ICI treatments, we here investigated the effect of bone marrow hematopoiesis in tumor-bearing Msh2loxP/loxP;TgTg(Vil1-cre) mice. The OS under anti-PD1 antibody treatment was 7.0 weeks (vs. 3.3 weeks and 5.0 weeks, control and isotype, respectively). In the anti-LAG-3 antibody group, OS was 13.3 weeks and thus even longer than in the anti-PD1 group (p = 0.13). Both ICIs induced a stable disease and reduced circulating and splenic regulatory T cells. In the bone marrow, a perturbed hematopoiesis was identified in tumor-bearing control mice, which was partially rescued by ICI treatment. In particular, B cell precursors and innate lymphoid progenitors were significantly increased upon anti-LAG-3 therapy to levels seen in tumor-free control mice. Additional normalizing effects of ICI treatment were observed for lin-c-Kit+IRF8+ hematopoietic stem cells, which function as a "master" negative regulator of the formation of polymorphonuclear-myeloid-derived suppressor cell generation. Accompanying immunofluorescence on the TME revealed significantly reduced numbers of CD206+F4/80+ and CD163+ tumor-associated M2 macrophages and CD11b+Gr1+ myeloid-derived suppressor cells especially upon anti-LAG-3 treatment. This study confirms the perturbed hematopoiesis in solid cancer. Anti-LAG-3 treatment partially restores normal hematopoiesis. The interference of anti-LAG-3 with suppressor cell populations in otherwise inaccessible niches renders this ICI very promising for subsequent clinical application.