A radiologist's guide to treatment response criteria in oncologic imaging: anatomic imaging biomarkers

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1

Purpose To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. Materials and Methods In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive. Each target node was measured using RECIST 1.1 criteria before and after merging or splitting, followed by volumetric segmentation. To compare RECIST 1.1 with volume, a single-dimension hypothetical diameter (HD) was determined from the nodal volume. The nodes were divided into three groups: (a) one-target merged (one target node merged with other nodes); (b) two-target merged (two neighboring target nodes merged); and (c) split node (a conglomerate node cleaved into smaller fragments). Bland-Altman analysis and t test were applied to compare RECIST 1.1 with HD. On the basis of the RECIST 1.1 concept, we compared response category changes between RECIST 1.1 and HD. Results The data set consisted of 30 merged nodes (19 one-target merged and 11 two-target merged) and 20 split nodes (mean age for all 50 included patients, 50 years ± 7 [standard deviation]; 38 men). RECIST 1.1, volumetric, and HD measurements indicated an increase in size in all one-target merged nodes. While volume and HD indicated an increase in size for nodes in the two-target merged group, RECIST 1.1 showed a decrease in size in all two-target merged nodes. Although volume and HD demonstrated a decrease in size of all split nodes, RECIST 1.1 indicated an increase in size in 60% (12 of 20) of the nodes. Discrepancy of the response categories between RECIST 1.1 and HD was observed in 5% (one of 19) in one-target merged, 82% (nine of 11) in two-target merged, and 55% (11 of 20) in split nodes. Conclusion RECIST 1.1 does not optimally reflect size changes when lymph nodes merge or split. Keywords: CT, Lymphatic, Tumor Response Supplemental material is available for this article. © RSNA, 2021.

Evaluation of risk classifications for gastrointestinal stromal tumor using multi-parameter Magnetic Resonance analysis

BACKGROUND

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal malignancy of the gastrointestinal tract. At present, it is generally believed that the prognosis of GIST is closely related to its risk classification. It may add value to correctly diagnose and evaluate the risk of invasion using a noninvasive imaging examination prior to surgery. MRI has the advantages of multiple parameters and high soft tissue resolution, which may be the potential method to preoperatively evaluate the risk of GIST.

PURPOSE

To retrospectively evaluate the diagnostic accuracy of multi-parameter MR analysis for preoperative risk classification of GIST.

MATERIALS AND METHODS

In this 6-year retrospective study, full MRI examination was performed on all 60 GIST cases confirmed classified by pathology, including 35 cases of very low-to-low-risk GIST and 25 cases of intermediate-to-high-risk GIST. Dynamic contrast-enhanced T1- and T2-weighted images, and apparent diffusion coefficient (ADC) maps were reviewed independently by two radiologists blinded to pathologic results. Volume, ADC ratio, three wash-in indexes (WII) were calculated and compared using t-test or Kruskal-Wallis nonparametric test. Sensitivity and specificity analyses were performed to calculate diagnostic accuracy using ROC analyses. Differences were considered significant at p < 0.05.

RESULTS

All GISTs were resected. Patient age, sex, tumor location and tumor shape did not differ significantly across the two groups (p = 0.798, 0.767, 0.822 and 0.096, respectively). GIST in the intermediate-to-high-risk group presented significantly greater volume (p = 0.0045), lower ADC ratio (p = 0.0125) and faster enhancement (for WII2, p < 0.0001; for WII3, p = 0.0358) than that of GIST in the very low-to-low-risk group. This combination of the volume, ADC ratio and WII2 provided sensitivity of 88%, specificity of 94.29%, and accuracy of 91.7% for the risk classification of GIST.

CONCLUSION

Multi-parameter MR analysis provides a preoperative imaging standard for accurately distinguishing very low-to-low-risk GIST from intermediate-to-high-risk GIST.

Cancer Clinical Trials: What Every Radiologist Wants to Know but Is Afraid to Ask

OBJECTIVE. The purpose of this article is to provide radiologists with a guide to the fundamental principles of oncology clinical trials. The review summarizes the evolution and structure of modern clinical trials with an emphasis on the relevance of clinical trials in the field of oncologic imaging. CONCLUSION. Understanding the structure and clinical relevance of modern clinical trials is beneficial for radiologists in the field of oncologic imaging.

Reassessing Patterns of Response to Immunotherapy with PET: From Morphology to Metabolism

Cancer demands precise evaluation and accurate and timely assessment of response to treatment. Imaging must be performed early during therapy to allow adjustments to the course of treatment. For decades, cross-sectional imaging provided these answers, showing responses to the treatment through changes in tumor size. However, with the emergence of immune checkpoint inhibitors, complex immune response patterns were revealed that have quickly highlighted the limitations of this approach. Patterns of response beyond tumor size have been recognized and include cystic degeneration, necrosis, hemorrhage, and cavitation. Furthermore, new unique patterns of response have surfaced, like pseudoprogression and hyperprogression, while other patterns were shown to be deceptive, such as unconfirmed progressive disease. This evolution led to new therapeutic evaluation criteria adapted specifically for immunotherapy. Moreover, inflammatory adverse effects of the immune checkpoint blockade were identified, many of which were life threatening and requiring prompt intervention. Given complex concepts like tumor microenvironment and novel therapeutic modalities in the era of personalized medicine, increasingly sophisticated imaging techniques are required to address the intricate patterns of behavior of different neoplasms. Fluorine 18-fluorodeoxyglucose PET/CT has rapidly emerged as one such technique that spans both molecular biology and immunology. This imaging technique is potentially capable of identifying and tracking prognostic biomarkers owing to its combined use of anatomic and metabolic imaging, which enables it to characterize biologic processes in vivo. This tailored approach may provide whole-body quantification of the metabolic burden of disease, providing enhanced prediction of treatment response and improved detection of adverse events. ^©RSNA, 2020.

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation

Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.

Apparent Diffusion Coefficient Value to Evaluate Tumor Response After Neoadjuvant Chemotherapy in Patients with Breast Cancer

RATIONALE AND OBJECTIVES

This study explored tumor behavior in patients with breast cancer during neoadjuvant chemotherapy (NAC) by sequential measurements of tumor apparent diffusion coefficient (ADC) after each chemotherapy cycle. The aim was to determine if the tumor ADC is useful to differentiate complete pathological response (cPR) from partial pathological response (pPR) during NAC.

MATERIALS AND METHODS

A total of 16 cases (in 14 patients) with diagnosis of breast cancer eligible to receive NAC were included. There were 70 magnetic resonance imaging examinations performed, 5 for each patient, during NAC cycles. Diffusion-weighted imaging was performed on a 1.5T system (b values of 0 and 700s/mm²). Four ADC ratios between the five MRI examinations were obtained to assess ADC changes during NAC. Absence of invasive breast cancer at surgical specimens (Miller-Payne 5) was considered as cPR and was used as reference for ADC cutoff ratios.

RESULTS

In this study, we were able to differentiate between cPR and pPR, after two cycles of NAC until the end of NAC before surgery (ADC ratios 2-4). The thresholds to differentiate between cPR and pPR of ADC ratios 2, 3, and 4, were 1.14 × 10^-3mm²/s, 1.08 × 10^-3mm²/s, and 1.25 × 10^-3mm²/s, respectively, and have a cross-validated sensitivity and specificity of 79.2%, 79.7% (ADC ratio 2); 100%, 66.7% (ADC ratio 3); and 100%, 83.8% (ADC ratio 4), respectively.

CONCLUSIONS

The ADC ratios were useful to differentiate cPR from pPR in breast cancer tumors after NAC. Thus, it may be useful in tailoring treatment in these patients.

Update on Gastrointestinal Stromal Tumors for Radiologists

The management of gastrointestinal stromal tumors (GISTs) has evolved significantly in the last two decades due to better understanding of their biologic behavior as well as development of molecular targeted therapies. GISTs with exon 11 mutation respond to imatinib whereas GISTs with exon 9 or succinate dehydrogenase subunit mutations do not. Risk stratification models have enabled stratifying GISTs according to risk of recurrence and choosing patients who may benefit from adjuvant therapy. Assessing response to targeted therapies in GIST using conventional response criteria has several potential pitfalls leading to search for alternate response criteria based on changes in tumor attenuation, volume, metabolic and functional parameters. Surveillance of patients with GIST in the adjuvant setting is important for timely detection of recurrences.

Accuracy and feasibility of estimated tumour volumetry in primary gastric gastrointestinal stromal tumours: validation using semiautomated technique in 127 patients

OBJECTIVES

To validate estimated tumour volumetry in primary gastric gastrointestinal stromal tumours (GISTs) using semiautomated volumetry.

METHODS

In this IRB-approved retrospective study, we measured the three longest diameters in x, y, z axes on CTs of primary gastric GISTs in 127 consecutive patients (52 women, 75 men, mean age 61 years) at our institute between 2000 and 2013. Segmented volumes (Vsegmented) were obtained using commercial software by two radiologists. Estimate volumes (V1-V6) were obtained using formulae for spheres and ellipsoids. Intra- and interobserver agreement of Vsegmented and agreement of V1-6 with Vsegmented were analysed with concordance correlation coefficients (CCC) and Bland-Altman plots.

RESULTS

Median Vsegmented and V1-V6 were 75.9, 124.9, 111.6, 94.0, 94.4, 61.7 and 80.3 cm(3), respectively. There was strong intra- and interobserver agreement for Vsegmented. Agreement with Vsegmented was highest for V6 (scalene ellipsoid, x ≠ y ≠ z), with CCC of 0.96 [95 % CI 0.95-0.97]. Mean relative difference was smallest for V6 (0.6 %), while it was -19.1 % for V5, +14.5 % for V4, +17.9 % for V3, +32.6 % for V2 and +47 % for V1.

CONCLUSION

Ellipsoidal approximations of volume using three measured axes may be used to closely estimate Vsegmented when semiautomated techniques are unavailable.

KEY POINTS

Estimation of tumour volume in primary GIST using mathematical formulae is feasible. Gastric GISTs are rarely spherical. Segmented volumes are highly concordant with three axis-based scalene ellipsoid volumes. Ellipsoid volume can be used as an alternative for automated tumour volumetry.