Computed Diffusion-Weighted Images of Rectal Cancer: Image Quality, Restaging, and Treatment Response after Neoadjuvant Therapy.

BACKGROUND: Computed diffusion-weighted images (cDWI) of random b value could be derived from acquired DWI (aDWI) with at least two different b values. However, its comparison between aDWI and cDWI images in locally advanced rectal cancer (LARC) patients after neoadjuvant therapy (NT) is needed. PURPOSE: To compare the cDWI and aDWI in image quality, restaging, and treatment response of LARC after NT. STUDY TYPE: Retrospective. POPULATION: Eighty-seven consecutive patients. FIELD STRENGTH/SEQUENCE: 3.0 T/DWI. ASSESSMENT: All patients underwent two DWI sequences, including conventional acquisition with b = 0 and 1000 s/mm2 (aDWIb1000 ) and another with b = 0 and 700 s/mm2 on a 3.0-T MR scanner. The images of the latter were used to compute the diffusion images with b = 1000 s/mm2 (cDWIb1000 ). Four radiologists with 3, 4, 14, and 25 years of experience evaluated the images to compare the image quality, TN restaging performance, and treatment response between aDWIb1000 and cDWIb1000 . STATISTICAL TESTS: Interclass correlation coefficients, weighted κ coefficient, paired Wilcoxon, and McNemar or Fisher test were used. A significance level of 0.05 was used. RESULTS: The cDWIb1000 images were superior to the aDWIb1000 ones in both subjective and objective image quality. In T restaging, the overall diagnostic accuracy of cDWIb1000 images was higher than that of aDWIb1000 images (57.47% vs. 49.43%, P = 0.289 for the inexperienced radiologist; 77.01% vs. 63.22%, significant for the experienced radiologist), with better sensitivity in determining ypT0-Tis tumors. Additionally, it increased the sensitivity in detecting ypT2 tumors for the inexperienced radiologist and ypT3 tumors for the experienced radiologist. N restaging and treatment response were found to be similar between two sequences for both radiologists. DATA CONCLUSION: Compared to aDWIb1000 images, the computed ones might serve as a wise approach, providing comparable or better image quality, restaging performance, and treatment response assessment for LARC after NT. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

CT-Based Radiomics Showing Generalization to Predict Tumor Regression Grade for Advanced Gastric Cancer Treated With Neoadjuvant Chemotherapy.

Objective: The aim of this study was to develop and validate a radiomics model to predict treatment response in patients with advanced gastric cancer (AGC) sensitive to neoadjuvant therapies and verify its generalization among different regimens, including neoadjuvant chemotherapy (NAC) and molecular targeted therapy. Materials and Methods: A total of 373 patients with AGC receiving neoadjuvant therapies were enrolled from five cohorts. Four cohorts of patients received different regimens of NAC, including three retrospective cohorts (training cohort and internal and external validation cohorts) and a prospective Dragon III cohort (NCT03636893). Another prospective SOXA (apatinib in combination with S-1 and oxaliplatin) cohort received neoadjuvant molecular targeted therapy (ChiCTR-OPC-16010061). All patients underwent computed tomography before treatment, and thereafter, tumor regression grade (TRG) was assessed. The primary tumor was delineated, and 2,452 radiomics features were extracted for each patient. Mutual information and random forest were used for dimensionality reduction and modeling. The performance of the radiomics model to predict TRG under different neoadjuvant therapies was evaluated. Results: There were 28 radiomics features selected. The radiomics model showed generalization to predict TRG for AGC patients across different NAC regimens, with areas under the curve (AUCs) (95% interval confidence) of 0.82 (0.76~0.90), 0.77 (0.63~0.91), 0.78 (0.66~0.89), and 0.72 (0.66~0.89) in the four cohorts, with no statistical difference observed (all p > 0.05). However, the radiomics model showed poor predictive value on the SOXA cohort [AUC, 0.50 (0.27~0.73)], which was significantly worse than that in the training cohort (p = 0.010). Conclusion: Radiomics is generalizable to predict TRG for AGC patients receiving NAC treatments, which is beneficial to transform appropriate treatment, especially for those insensitive to NAC.

Comparison of Computed and Acquired DWI in the Assessment of Rectal Cancer: Image Quality and Preoperative Staging.

Objective: The aim of the study was to evaluate the computed diffusion-weighted images (DWI) in image quality and diagnostic performance of rectal cancer by comparing with the acquired DWI. Methods: A total of 103 consecutive patients with primary rectal cancer were enrolled in this study. All patients underwent two DWI sequences, namely, conventional acquisition with b = 0 and 1,000 s/mm2 (aDWIb1,000) and another with b = 0 and 700 s/mm2 on a 3.0T MR scanner (MAGNETOM Prisma; Siemens Healthcare, Germany). The images (b = 0 and 700 s/mm2) were used to compute the diffusion images with b value of 1,000 s/mm2 (cDWIb1,000). Qualitative and quantitative analysis of both computed and acquired DWI images was performed, namely, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and signal intensity ratio (SIR), and also diagnostic staging performance. Interclass correlation coefficients, weighted κ coefficient, Friedman test, Wilcoxon paired test, and McNemar or Fisher test were used for repeatability and comparison assessment. Results: Compared with the aDWIb1,000 images, the cDWIb1,000 ones exhibited significant higher scores of subjective image quality (all P <0.050). SNR, SIR, and CNR of the cDWIb1,000 images were superior to those of the aDWIb1,000 ones (P <0.001). The overall diagnostic accuracy of computed images was higher than that of the aDWIb1,000 images in T stage (P <0.001), with markedly better sensitivity and specificity in distinguishing T1-2 tumors from the T3-4 ones (P <0.050). Conclusion: cDWIb1,000 images from lower b values might be a useful alternative option and comparable to the acquired DWI, providing better image quality and diagnostic performance in preoperative rectal cancer staging.

Expression profiling of CPS1 in Correa's cascade and its association with gastric cancer prognosis.

Carbamoyl phosphate synthetase 1 (CPS1), which is the antigen for the hepatocyte paraffin 1 antibody, exhibits focal immunoreactivity in adenocarcinoma from the gastrointestinal tract, but its expression profiles and roles in gastric cancer (GC) remain largely unknown. The present study aimed to determine the expression pattern and prognostic value of CPS1 in Correa's cascade using tissues from 32 patients with chronic atrophic gastritis with intestinal metaplasia (IM), 62 patients with low- or high-grade intraepithelial neoplasia (IN) and 401 patients with GC. The expression of CPS1 was diffuse and strongly positive in 32 cases (100%) of IM of the glandular epithelium, and gradually downregulated in Correa's cascade, with a strongly positive ratio of 21 (70%) in low-grade IN and 4 (12.5%) in high-grade IN. The levels of CPS1 expression were significantly higher in diffuse-type GC, with 37 (26%) cases strongly positive for CPS1, compared with 14 (8%) in intestinal-type and 11 (13%) cases in mixed-type GC. In intestinal-type GC, CPS1 expression was completely lost in 107 (62%) of cases, which was associated with an advanced Tumor-Node-Metastasis stage (P=0.031) and depth of invasion (P=0.037). Kaplan-Meier analysis suggested that low CPS1 expression levels were independently associated with a short overall survival (OS) time in the three types of GC (P<0.001 in intestinal-type, P=0.003 in diffuse-type and P=0.018 in mixed-type GC). Furthermore, low levels of CPS1 mRNA and high methylation levels in the CPS1 promoter were associated with a short OS time in patients with GC. These results suggested that the expression of CPS1 was progressively downregulated in Correa's cascade, and that CPS1 may serve as a prognostic marker for patients with GC, regardless of tumor type.

Prediction Model of Tumor Regression Grade for Advanced Gastric Cancer After Preoperative Chemotherapy.

BACKGROUND: Preoperative chemotherapy (PCT) has been considered an important treatment for advanced gastric cancer (AGC). The tumor regression grade (TRG) system is an effective tool for the assessment of patient responses to PCT. Pathological complete response (TRG = 0) of the primary tumor is an excellent predictor of better prognosis. However, which patients could achieve pathological complete response (TRG = 0) after chemotherapy is still unknown. The study aimed to find predictors of TRG = 0 in AGC. METHODS: A total of 304 patients with advanced gastric cancer from July 2009 to November 2018 were enrolled retrospectively. All patients were randomly assigned (2:1) to training and internal validation groups. In addition, 124 AGC patients receiving PCT from December 2018 to June 2020 were included prospectively in the external validation cohort. A prediction model for TRG = 0 was established based on four predictors in the training group and was validated in the internal and external validation groups. RESULTS: Through univariate and multivariate analyses, we found that CA199, CA724, tumor differentiation and short axis of the largest regional lymph node (LNmax) were independent predictors of TRG = 0. Based on the four predictors, we established a prediction model for TRG = 0. The AUC values of the prediction model in the training, internal and external validation groups were 0.84, 0.73 and 0.82, respectively. CONCLUSIONS: We found that CA199, CA724, tumor differentiation and LNmax were associated with pathological response in advanced gastric cancer. The prediction model could provide guidance for clinical work.

The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images.

OBJECTIVES: The aim was to determine whether the dual-energy CT radiomics model derived from an iodine map (IM) has incremental diagnostic value for the model based on 120-kV equivalent mixed images (120 kVp) in preoperative restaging of serosal invasion with locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC). METHODS: A total of 155 patients (110 in the training cohort and 45 in the testing cohort) with LAGC who had standard NAC before surgery were retrospectively enrolled. All CT images were analyzed by two radiologists for manual classification. Volumes of interests (VOIs) were delineated semi-automatically, and 1,226 radiomics features were extracted from every segmented lesion in both IM and 120 kVp images, respectively. Spearman's correlation analysis and the least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented for filtering unstable and redundant features and screening out vital features. Two predictive models (120 kVp and IM-120 kVp) based on 120 kVp selected features only and 120 kVp combined with IM selected features were established by multivariate logistic regression analysis. We then build a combination model (ComModel) developed with IM-120 kVp signature and ycT. The performance of these three models and manual classification were evaluated and compared. RESULT: Three radiomics models showed great predictive accuracy and performance in both the training and testing cohorts (ComModel: AUC: training, 0.953, testing, 0.914; IM-120 kVp: AUC: training, 0.953, testing, 0.879; 120 kVp: AUC: training, 0.940, testing, 0.831). All these models showed higher diagnostic accuracy (ComModel: 88.9%, IM-120 kVp: 84.4%, 120 kVp: 80.0%) than manual classification (68.9%) in the testing group. ComModel and IM-120 kVp model had better performances than manual classification both in the training (both p<0.001) and testing cohorts (p<0.001 and p=0.034, respectively). CONCLUSIONS: Dual-energy CT-based radiomics models demonstrated convincible diagnostic performance in differentiating serosal invasion in preoperative restaging for LAGC. The radiomics features derived from IM showed great potential for improving the diagnostic capability.

[Pathological evaluation system for preoperative treatment of gastric cancer].

As the perioperative chemotherapy and conversion therapy has been widely implemented on the standard treatment of gastric cancer patients, it is of great importance to evaluate the efficacy of these patients accurately by effective methods. Being an important grading standard for histomorphological evaluation of excision specimens after neoadjuvant chemotherapy, pathological tumor regression grade (TRG) system is mainly used to assess the degree of fibrosis of tumor tissue and the proportion of residual tumor cells. TRG evaluation may provide important information referring to clinical decision making and prognostic judgment, and may imply on different efficacy and survival rates. Currently, four TRG standards can be used to evaluate the efficacy of neoadjuvant chemotherapy or translational therapy for primary gastric cancer, including Becker, Mandard, Ninomiya and Ryan, among which Ryan's 0-3 classification system is the most widely used. The main factors influencing the outcome of postoperative pathological TRG evaluation of gastric cancer include tumor localization, macroscopic observation and dissection of specimens, microscopic evaluation, as well as TRG evaluation criteria that are too complicated and difficult to operate. Although some studies have found that tumor regression of gastric cancer may be associated with some molecular markers, it may bring greater benefits to the choice of treatment decisions and prognosis judgment if further studies can confirm that specific biomarkers can help estimate the efficacy of neoadjuvant chemotherapy and translational therapy for gastric cancer after endoscopic biopsy.

Locally advanced gastric cancer: total iodine uptake to predict the response of primary lesion to neoadjuvant chemotherapy.

PURPOSE: Pathologic response to neoadjuvant chemotherapy is a prognostic factor in many cancer types. However, the existing evaluative criteria are deficient. We sought to prospectively evaluate the total iodine uptake derived from dual-energy computed tomography (DECT) in predicting treatment efficacy and progression-free survival (PFS) time in gastric cancer after neoadjuvant chemotherapy. METHODS: From October 2012 to December 2015, 44 patients with locally advanced gastric cancer were examined with DECT 1 week before and three cycles after neoadjuvant chemotherapy. The percentage changes in tumor area (%ΔS), diameter (%ΔD), and density (%ΔHU) were calculated to evaluate the WHO, RESCIST, and Choi criteria. The percentage changes in tumor volume (%ΔV) and total iodine uptake of portal phase (%ΔTIU-p) were also calculated to determine cut-off values by ROC curves. The correlation between the different criteria and histopathologic tumor regression grade (Becker score) or PFS were statistically analyzed. RESULTS: Forty-four patients were divided into responders and non-responders according to 43.34% volume reduction (P = 0.002) and 63.87% (P = 0.002) TIU-p reduction, respectively. The %ΔTIU-p showed strong (r = 0.602, P = 0.000) and %ΔV showed moderate (r = 0.416, P = 0.005), while the WHO (r = 0.075, P = 0.627), RECIST (r = 0.270, P = 0.077) and Choi criteria (r = 0.238, P = 0.120) showed no correlation with the Becker score. The differences in PFS time between the responder and non-responder groups were significant according to %ΔTIU-p and Choi criteria (P = 0.001 and P = 0.013, respectively). CONCLUSIONS: The TIU-p can help predict pathological regression in advanced gastric cancer patients after neoadjuvant chemotherapy. In addition, the %ΔTIU-p could be one of the potentially valuable predictive parameters of the PFS time.