The repeatability and consistency of different methods for measuring the volume parameters of the primary rectal cancer on diffusion weighted images.

Background: To determine the reproducibility of measuring the gross total volume (GTV) of primary rectal tumor with manual and semi-automatic delineation on the diffusion-weighted image (DWI), examine the consistency of using the same delineation method on DWI images with different high b-values, and find the optimal delineation method to measure the GTV of rectal cancer. Methods: 41 patients who completed rectal MR examinations in our hospital from January 2020 to June 2020 were prospectively enrolled in this study. The post-operative pathology confirmed the lesions were rectal adenocarcinoma. The patients included 28 males and 13 females, with an average age of (63.3 ± 10.6) years old. Two radiologists used LIFEx software to manually delineate the lesion layer by layer on the DWI images (b=1000 s/mm2 and 1500 s/mm2) and used 10% to 90% of the highest signal intensity as thresholds to semi-automatically delineate the lesion and measure the GTV. After one month, Radiologist 1 performed the same delineation work again to obtain the corresponding GTV. Results: The inter- and intra-observer interclass correlation coefficients (ICC) of measuring GTV using semi-automatic delineation with 30% to 90% as thresholds were all >0.900. There was a positive correlation between manual delineation and semi-automatic delineation with 10% to 50% thresholds (P < 0.05). However, the manual delineation was not correlated with the semi-automatic delineation with 60%, 70%, 80%, and 90% thresholds. On the DWI images with b=1000 s/mm2 and 1500 s/mm2, the 95% limit of agreement (LOA%) of measuring GTV using semi-automatic delineation with 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% thresholds were -41.2~67.4, -17.8~51.5, -16.1~49.3, -26.2~50.1, -42.3~57.6, -57.1~65.4, -67.3~66.5, -101.6~91.1, -129.4~136.0, and -15.3~33.0, respectively. The time required for GTV measurement by semi-automatic delineation was significantly shorter than that of manual delineation (12.9 ± 3.6s vs 40.2 ± 13.1s). Conclusions: The semi-automatic delineation of rectal cancer GTV with 30% threshold had high repeatability and consistency, and it was positively correlated with the GTV measured by manual delineation. Therefore, the semi-automatic delineation with 30% threshold could be a simple and feasible method for measuring rectal cancer GTV.

High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer.

PURPOSE: To establish and validate two predictive radiomics models for preoperative prediction of lymph node metastases (LNMs) and tumor deposits (TDs) respectively in rectal cancer (RC) patients. METHODS: A total of 139 RC patients (98 in the training cohort and 41 in the validation cohort) were enrolled in the present study. High-resolution magnetic resonance images (HRMRI) were retrieved for tumor segmentation and feature extraction. HRMRI findings of RC were assessed by three experienced radiologists. Two radiomics nomograms were established by integrating the clinical risk factors, HRMRI findings and radiomics signature. RESULTS: The predictive nomogram of LNMs showed good predictive performance (area under the curve [AUC], 0.90; 95% confidence interval [CI] 0.83-0.96) which was better than clinico-radiological (AUC, 0.83; 95% CI 0.74-0.93; Delong test, p = 0.017) or radiomics signature-only model (AUC, 0.77; 95% CI 0.67-0.86; Delong test, p = 0.003) in training cohort. Application of the nomogram in the validation cohort still exhibited good performance (AUC, 0.87; 95% CI 0.76-0.98). The accuracy, sensitivity and specificity of the combined model in predicting LNMs was 0.86,0.79 and 0.91 in training cohort and 0.83,0.85 and 0.82 in validation cohort. As for TDs, the predictive efficacy of the nomogram (AUC, 0.82; 95% CI 0.71-0.93) was not significantly higher than radiomics signature-only model (AUC, 0.80; 95% CI 0.69-0.92; Delong test, p = 0.71). Radiomics signature-only model was adopted to predict TDs with accuracy=0.76, sensitivity=0.72 and specificity=0.94 in training cohort and 0.68, 0.62 and 0.97 in validation cohort. CONCLUSION: HRMRI-based radiomics models could be helpful for the prediction of LNMs and TDs preoperatively in RC patients.

High resolution MRI-based radiomic nomogram in predicting perineural invasion in rectal cancer.

BACKGROUND: To establish and validate a high-resolution magnetic resonance imaging (HRMRI)-based radiomic nomogram for prediction of preoperative perineural invasion (PNI) of rectal cancer (RC). METHODS: Our retrospective study included 140 subjects with RC (99 in the training cohort and 41 in the validation cohort) who underwent a preoperative HRMRI scan between December 2016 and December 2019. All subjects underwent radical surgery, and then PNI status was evaluated by a qualified pathologist. A total of 396 radiomic features were extracted from oblique axial T2 weighted images, and optimal features were selected to construct a radiomic signature. A combined nomogram was established by incorporating the radiomic signature, HRMRI findings, and clinical risk factors selected by using multivariable logistic regression. RESULTS: The predictive nomogram of PNI included a radiomic signature, and MRI-reported tumor stage (mT-stage). Clinical risk factors failed to increase the predictive value. Favorable discrimination was achieved between PNI-positive and PNI-negative groups using the radiomic nomogram. The area under the curve (AUC) was 0.81 (95% confidence interval [CI], 0.71-0.91) in the training cohort and 0.75 (95% CI, 0.58-0.92) in the validation cohort. Moreover, our result highlighted that the radiomic nomogram was clinically beneficial, as evidenced by a decision curve analysis. CONCLUSIONS: HRMRI-based radiomic nomogram could be helpful in the prediction of preoperative PNI in RC patients.

[Metabolic flux analysis of L-valine fermentation in Corynebacterium glutamicum].

In industrial fermentation of amino acids the cells are often forced to synthesize the biochemicals excessive of their physiological needs. The knowledge of metabolic networks and their regulation relevant usually come from biochemical research, especially from enzymology, not from engineering study. To enrich the knowledge of metabolic sub-network of L-valine syntheses for higher production of L-valine, Corynebacterium glutamicum AS1.495 and its genetic derivatives AA361, AAT231, AATV341 were used for metabolic flux analysis. AS1.495 is a leucine auxotrophic (Leu-), and the three derivatives carry additional mutations. AA361 contains D-aspartic acid-beta-hydroxamate supersensitive marker (Leu-, L-AAHss), AAT231 (Leu-, L-AAHss, 2-TAr) is D-aspartic acid-beta-hydroxamate supersensitive and 2-thiazole alanine resistant, and AAT341 (Leu-, L-AAHss, 2-TAr, Vd-) is a D-aspartic acid-beta-hydroxamate supersensitive, 2-thiazole alanine resistant and valine-decompose-ability imperfect (Vd-). The concentrations of extra-cellular metabolites were determined under sub-steady-state of the batch culture. The metabolic flux distribution maps of the four strains were obtained, compared and analyzed. Our analysis showed that the flux ratio of EMP and HMP from the glucose-6-phosphate had increased from 0.205 in the parental strain AS1.495 to 0.321 in the multiple-mutation strain AATV341; the flux ratio of L-valine synthesis branch and the rest branches from the pyruvate node increased from 0.188 in AS1.495 to 3.29 in AATV341; the flux of lactic acid synthesis branch decreased from 11.1 in AS1.495 to 1.16 in AATV341; the flux of L-valine synthesis branch increased from 5.37 in AS1.495 to 37.3 in AATV341; and the productivity of L-valine correspondently increased from 4 g/L in AS1.495 to 24.5 g/L in AATV341. These results indicate that the introduction of analog supersensitive marker L-AAH55 and/or analog resistant marker 2-TAr skew the metabolic flux towards the formation of L-valine. This study revealed the usefulness of the metabolic flux analysis as a tool for verification of existing production strains. The analysis may play an important role in helping us b to rationally re-design metabolism for further improvement of fermentation process.