New response evaluation criteria using early morphological change in imatinib treatment for patients with gastrointestinal stromal tumor

CT features combined with RECIST 1.1 criteria improve progression assessments of sunitinib-treated gastrointestinal stromal tumors

OBJECTIVES

To explore the auxiliary value of combining CT features with existing response evaluation criteria in the prediction of progressive disease (PD) in gastrointestinal stromal tumors (GIST) patients treated with sunitinib.

MATERIAL AND METHODS

Eighty-one patients with GISTs who received sunitinib were included in this retrospective multicenter study and divided into training and external validation cohorts. Progression at six months was determined as a reference standard. The predictive performance of the RECIST 1.1 and Choi criteria was compared. CT features at baseline and the first follow-up were analyzed. Logistic regression analyses were used to determine the most significant predictors and develop modified criteria.

RESULTS

A total of 216 lesions showed a good response and 107 showed a poor response in 81 patients. The RECIST 1.1 criteria performed better than the Choi criteria in predicting progression (AUC, 0.75 vs. 0.69, p = 0.04). The expanded/intensified high-enhancement area, blurred tumor-tissue interface, and progressive enlarged vessels feeding or draining the mass (EVFDM) differed significantly between lesions with good and poor responses in the training cohort (p = 0.001, 0.003, and 0.000, respectively). Multivariate analysis revealed that the expanded/intensified high-enhancement area (p = 0.001), progressive EVFDM (p = 0.000), and RECIST PD (p = 0.000) were independent predictive factors. Modified RECIST (mRECIST) criteria were developed and showed significantly higher AUCs in the training and external validation cohorts than the RECIST 1.1 criteria (training: 0.81 vs. 0.73, p = 0.002; validation: 0.82 vs. 0.77, p = 0.04).

CONCLUSION

The mRECIST criteria, combining CT features with the RECIST 1.1 criteria, demonstrated superior performance in the prediction of early progression in GIST patients receiving sunitinib.

CLINICAL RELEVANCE STATEMENT

The mRECIST criteria, which combine CT features with the RECIST 1.1 criteria, may facilitate the early detection of progressive disease in GIST patients treated with sunitinib, thereby potentially guiding the timely switch to late-line medications or combination with surgical excision.

KEY POINTS

• The RECIST 1.1 criteria outperformed the Choi criteria in identifying progression of GISTs in patients treated with sunitinib. • GISTs displayed different morphologic features on CT depending on how they responded to sunitinib. • Combining CT morphologic features with the RECIST 1.1 criteria allowed for the prompt and accurate identification of progressing GIST lesions.

Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors

OBJECTIVES

To establish and validate a radiomics nomogram model for preoperative prediction of KIT exon 9 mutation status in patients with gastrointestinal stromal tumors (GISTs).

MATERIALS AND METHODS

Eighty-seven patients with pathologically confirmed GISTs were retrospectively enrolled in this study. Imaging and clinicopathological data were collected and randomly assigned to the training set (n = 60) and test set (n = 27) at a ratio of 7:3. Based on contrast-enhanced CT (CE-CT) arterial and venous phase images, the region of interest (ROI) of the tumors were manually drawn layer by layer, and the radiomics features were extracted. The intra-class correlation coefficient (ICC) was used to test the consistency between observers. Least absolute shrinkage and selection operator regression (LASSO) were used to further screen the features. The nomogram of integrated radiomics score (Rad-Score) and clinical risk factors (extra-gastric location and distant metastasis) was drawn on the basis of multivariate logistic regression. The area under the receiver operating characteristic (AUC) curve and decision curve analysis were used to evaluate the predictive efficiency of the nomogram, and the clinical benefits that the decision curve evaluation model may bring to patients.

RESULTS

The selected radiomics features (arterial phase and venous phase features) were significantly correlated with the KIT exon 9 mutation status of GISTs. The AUC, sensitivity, specificity, and accuracy in the radiomics model were 0.863, 85.7%, 80.4%, and 85.0% for the training group (95% confidence interval [CI]: 0.750-0.938), and 0.883, 88.9%, 83.3%, and 81.5% for the test group (95% CI: 0.701-0.974), respectively. The AUC, sensitivity, specificity, and accuracy in the nomogram model were 0.902 (95% confidence interval [CI]: 0.798-0.964), 85.7%, 86.9%, and 91.7% for the training group, and 0.907 (95% CI: 0.732-0.984), 77.8%, 94.4%, and 88.9% for the test group, respectively. The decision curve showed the clinical application value of the radiomic nomogram.

CONCLUSION

The radiomics nomogram model based on CE-CT can effectively predict the KIT exon 9 mutation status of GISTs and may be used for selective gene analysis in the future, which is of great significance for the accurate treatment of GISTs.