Automated segmentation of brain metastases with deep learning: a multi-center, randomized crossover, multi-reader evaluation study.

BACKGROUND: Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation. METHODS: A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10,338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at five centers. Five radiology residents and five attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared. RESULTS: The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (p = 0.67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (p < 0.001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs. 0.03 [0.03-0.03]; p < 0.001), but a similar time reduction (reduced median time, 44% [40-47%] vs. 40% [37-44%]; p = 0.92) with BMSS assistance. CONCLUSIONS: The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.

Prediction of microvascular invasion in hepatocellular carcinoma patients with MRI radiomics based on susceptibility weighted imaging and T2-weighted imaging.

BACKGROUND: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance. PURPOSE: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients. MATERIALS AND METHODS: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation. RESULTS: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948). CONCLUSION: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits.

Correlation of Ktrans derived from dynamic contrast-enhanced MRI with treatment response and survival in locally advanced NSCLC patients undergoing induction immunochemotherapy and concurrent chemoradiotherapy.

PURPOSE: This study aimed to investigate the prognostic significance of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters concerning tumor response following induction immunochemotherapy and survival outcomes in patients with locally advanced non-small cell lung cancer (NSCLC) who underwent immunotherapy-based multimodal treatments. MATERIAL AND METHODS: Unresectable stage III NSCLC patients treated by induction immunochemotherapy, concurrent chemoradiotherapy (CCRT) with or without consolidative immunotherapy from two prospective clinical trials were screened. Using the two-compartment Extend Tofts model, the parameters including Ktrans, Kep, Ve, and Vp were calculated from DCE-MRI data. The apparent diffusion coefficient was calculated from diffusion-weighted-MRI data. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to assess the predictive performance of MRI parameters. The Cox regression model was used for univariate and multivariate analysis. RESULTS: 111 unresectable stage III NSCLC patients were enrolled. Patients received two cycles of induction immunochemotherapy and CCRT, with or without consolidative immunotherapy. With the median follow-up of 22.3 months, the median progression-free survival (PFS) and overall survival (OS) were 16.3 and 23.8 months. The multivariate analysis suggested that Eastern Cooperative Oncology Group score, TNM stage and the response to induction immunochemotherapy were significantly related to both PFS and OS. After induction immunochemotherapy, 67 patients (59.8%) achieved complete response or partial response and 44 patients (40.2%) had stable disease or progressive disease. The Ktrans of primary lung tumor before induction immunochemotherapy yielded the best performance in predicting the treatment response, with an AUC of 0.800. Patients were categorized into two groups: high-Ktrans group (n=67, Ktrans＞164.3×10-3/min) and low-Ktrans group (n=44, Ktrans≤164.3×10-3/min) based on the ROC analysis. The high-Ktrans group had a significantly higher objective response rate than the low-Ktrans group (85.1% (57/67) vs 22.7% (10/44), p<0.001). The high-Ktrans group also presented better PFS (median: 21.1 vs 11.3 months, p=0.002) and OS (median: 34.3 vs 15.6 months, p=0.035) than the low-Ktrans group. CONCLUSIONS: Pretreatment Ktrans value emerged as a significant predictor of the early response to induction immunochemotherapy and survival outcomes in unresectable stage III NSCLC patients who underwent immunotherapy-based multimodal treatments. Elevated Ktrans values correlated positively with enhanced treatment response, leading to extended PFS and OS durations.

Evaluating contouring accuracy and dosimetry impact of current MRI-guided adaptive radiation therapy for brain metastases: a retrospective study.

BACKGROUND: Magnetic resonance imaging (MRI) guided adaptive radiotherapy (MRgART) has gained increasing attention, showing clinical advantages over conventional radiotherapy. However, there are concerns regarding online target delineation and modification accuracy. In our study, we aimed to investigate the accuracy of brain metastases (BMs) contouring and its impact on dosimetry in 1.5 T MRI-guided online adaptive fractionated stereotactic radiotherapy (FSRT). METHODS: Eighteen patients with 64 BMs were retrospectively evaluated. Pre-treatment 3.0 T MRI scans (gadolinium contrast-enhanced T1w, T1c) and initial 1.5 T MR-Linac scans (non-enhanced online-T1, T2, and FLAIR) were used for gross target volume (GTV) contouring. Five radiation oncologists independently contoured GTVs on pre-treatment T1c and initial online-T1, T2, and FLAIR images. We assessed intra-observer and inter-observer variations and analysed the dosimetry impact through treatment planning based on GTVs generated by online MRI, simulating the current online adaptive radiotherapy practice. RESULTS: The average Dice Similarity Coefficient (DSC) for inter-observer comparison were 0.79, 0.54, 0.59, and 0.64 for pre-treatment T1c, online-T1, T2, and FLAIR, respectively. Inter-observer variations were significantly smaller for the 3.0 T pre-treatment T1c than for the contrast-free online 1.5 T MR scans (P < 0.001). Compared to the T1c contours, the average DSC index of intra-observer contouring was 0.52‒0.55 for online MRIs. For BMs larger than 3 cm3, visible on all image sets, the average DSC indices were 0.69, 0.71 and 0.64 for online-T1, T2, and FLAIR, respectively, compared to the pre-treatment T1c contour. For BMs < 3 cm3, the average visibility rates were 22.3%, 41.3%, and 51.8% for online-T1, T2, and FLAIR, respectively. Simulated adaptive planning showed an average prescription dose coverage of 63.4‒66.9% when evaluated by ground truth planning target volumes (PTVs) generated on pre-treatment T1c, reducing it from over 99% coverage by PTVs generated on online MRIs. CONCLUSIONS: The accuracy of online target contouring was unsatisfactory for the current MRI-guided online adaptive FSRT. Small lesions had poor visibility on 1.5 T non-contrast-enhanced MR-Linac images. Contour inaccuracies caused a one-third drop in prescription dose coverage for the target volume. Future studies should explore the feasibility of contrast agent administration during daily treatment in MRI-guided online adaptive FSRT procedures.

Longitudinal changes in renal parenchymal volume and function status after partial nephrectomy: a retrospective cohort study.

BACKGROUND: The ipsilateral renal parenchymal volume (RPV) experiences a sharp decrease shortly after partial nephrectomy (PN), mainly due to surgical remove or devascularization of kidney tissue. However, the subsequent change of RPV and its association with glomerular filtration rate (GFR) fast decline remains unknown. Our objective was to investigate the change of ipsilateral RPV and renal function status from new baseline (1-12 months after PN) to latest follow-up (≥1 year) after PN, and to explore factors associated with ipsilateral RPV decrease rate and correlation between RPV decrease and GFR fast decline. MATERIALS AND METHODS: A retrospective review of 367 patients with PN was conducted. Three-dimensional reconstruction of computed tomography (CT)/MRI images was performed for RPV calculation. Spectrum score was used to assess the degree of acute kidney injury (AKI) in the operated kidney after PN. GFR decline greater than 3 ml/min/1.73 m 2 /year was defined as GFR fast decline. One hundred fourteen patients underwent abdominal surgery was used as control. Predictive factors for subsequent decrease of RPV rate and GFR fast decline were evaluated by linear and logistic regression, respectively. RESULTS: With a median interval time of 21.1 (interquartile range:13.8-35.5) months, median ipsilateral RPV significantly decreased from 118.7 (interquartile range:100.7-137.1) ml at new baseline to 111.8 (IQR: 92.3-131.3) ml at latest follow-up. The interval time [ß: 1.36(0.71-2.01), P <0.001] and spectrum score [ß: 5.83 (2.92-8.74), P <0.001] were identified as independent predictors of ipsilateral RPV decrease rate. GFR fast decline was observed in 101 (27.5%) patients. Annual ipsilateral RPV decrease rate [odds ratio:1.67 (1.05-2.67), P =0.03] and overweight [odds ratio:1.63 (1.02-2.60), P =0.04] were independent predictors of GFR fast decline. CONCLUSIONS: Ipsilateral RPV experienced a moderate but significant decrease during follow-up after PN, especially in those with severer acute kidney injury. The presence of GFR fast decline was found to be associated with reduction of ipsilateral RPV, particularly in overweight individuals.

Association of computed tomography-based body composition with survival in metastatic renal cancer patient received immunotherapy: a multicenter, retrospective study.

OBJECTIVES: To investigate the association of computed tomography-assessed body composition with survival outcomes of metastatic renal cell carcinoma (mRCC) received immunotherapy. METHODS: In this multicenter, retrospective study, we reviewed 251 mRCC patients who received anti-PD1 from five centers. We analyzed the relationship between BMI, skeletal muscle area (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and subcutaneous adipose percentage (SAT%) with progression-free survival (PFS) and overall survival (OS). The spatial localization T cells was investigated by multiplex immunofluorescence. RESULTS: Among 224 evaluable patients, 23 (10.3%) patients were underweight, 118 (52.7%) had normal weight, 65 (29%) were overweight, and 18 patients (8%) were obese. The median age was 55 years and most patients were male (71%). No significant improvement in PFS (HR, 0.61; 95% CI, 0.27-1.42) or OS (HR, 1.09; 95% CI, 0.38-3.13) was observed for the obese patients. Besides, SM, VAT, and SAT were not associated with survival outcomes (all p > 0.05). Interestingly, SAT% independently predicted PFS (as continuous variable, HR: 0.02; 95% CI, 0.01-0.11) and OS (HR:0.05; 95% CI, 0.01-0.39), which remained significant in multivariate modeling (as continuous variable, adjusted HR for PFS, 0.01; 95% CI, 0.00-0.04; adjusted HR for OS, 0.08; 95% CI, 0.01-0.72). These associations were consistent in subgroup analysis of different gender, BMI, PD-L1 positive, and sarcopenia group. Tumor of high SAT% patients had a higher intratumoral PD1+ CD8+ T cell density and ratio. CONCLUSION: High SAT% predicts better outcomes in mRCC patients treated with anti-PD1 and T cell location may account for the better response. KEY POINTS: • CT-based subcutaneous adipose percentage independently predicted progression-free survival and overall survival. • Patients with a higher subcutaneous adipose percentage had a higher intratumoral PD1+ CD8+ T cell density and ratio.

False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study.

BACKGROUND: Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers. METHODS: A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1,066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression. RESULTS: The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees (P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers. CONCLUSIONS: Despite the improvement in detection performance, attention should be paid to FPs and small lesions with lower enhancement for radiologists, especially for less-experienced radiologists.

Evaluation of the efficacy and feasibility of concurrent weekly docetaxel-nedaplatin and hypo-fractionated radiotherapy in atypical histologic subtypes of primary and metastatic mediastinal malignancies.

Background: We aimed to evaluate the efficacy and feasibility of concurrent weekly docetaxel-nedaplatin and hypo-fractionated radiotherapy (hypo-RT) in atypical histologic subtypes of primary and metastatic mediastinal malignancies. Methods: Fifty-four patients diagnosed with atypical primary or metastatic mediastinal malignancies were retrospectively reviewed. 30 patients received concurrent weekly docetaxel and nedaplatin and hypo-RT (CChRT group) and 24 patients had hypo-RT alone (hRT group). Overall response rate (ORR), in-field locoregional progression-free survival (LPFS) and toxicities were analyzed. The radiobiological effect was evaluated by the LQRGC/TCP model, incorporating four "R"s of radiobiology, Gompertzian tumor growth and radio-sensitizing effect of chemotherapeutic agent. The biologically effective doses (BEDs) were calculated. Results: The median follow-up time was 29.2 months for all patients. The ORR was 86.7% in CChRT group, compared with 62.5% in hRT group (p=0.033). The 2-year in-field LPFS of CChRT and hRT group was 73.4% and 47.3%, respectively (p=0.003). There was no significant difference of any >=Grade 3 toxicities between the two groups (p=0.754). The mean total dose and mean BED by the LQRGC/TCP model in CChRT group were 58.2Gy and 72.34Gy, versus 52.6Gy and 67.25Gy in hRT group. Conclusions: Concurrent weekly docetaxel-nedaplatin and hypo-RT achieved promising in-field LPFS and tolerable toxicities compared with hypo-RT alone in different histologic subtypes of primary and metastatic mediastinal malignancies.

Radiomic Signatures for Predicting EGFR Mutation Status in Lung Cancer Brain Metastases.

Background: Lung cancer is the most common primary tumor metastasizing to the brain. A significant proportion of lung cancer patients show epidermal growth factor receptor (EGFR) mutation status discordance between the primary cancer and the corresponding brain metastases, which can affect prognosis and therapeutic decision-making. However, it is not always feasible to obtain brain metastases samples. The aim of this study was to establish a radiomic model to predict the EGFR mutation status of lung cancer brain metastases. Methods: Data from 162 patients with resected brain metastases originating from lung cancer (70 with mutant EGFR, 92 with wild-type EGFR) were retrospectively analyzed. Radiomic features were extracted using preoperative brain magnetic resonance (MR) images (contrast-enhanced T1-weighted imaging, T1CE; T2-weighted imaging, T2WI; T2 fluid-attenuated inversion recovery, T2 FLAIR; and combinations of these sequences), to establish machine learning-based models for predicting the EGFR status of excised brain metastases (108 metastases for training and 54 metastases for testing). The least absolute shrinkage selection operator was used to select informative features; radiomics models were built with logistic regression of the training cohort, and model performance was evaluated using an independent test set. Results: The best-performing model was a combination of 10 features selected from multiple sequences (two from T1CE, five from T2WI, and three from T2 FLAIR) in both the training and test sets, resulting in classification area under the curve, accuracy, sensitivity, and specificity values of 0.85 and 0.81, 77.8% and 75.9%, 83.7% and 73.1%, and 73.8% and 78.6%, respectively. Conclusions: Radiomic signatures integrating multi-sequence MR images have the potential to noninvasively predict the EGFR mutation status of lung cancer brain metastases.

Radiomic Signatures for Predicting Receptor Status in Breast Cancer Brain Metastases.

Backgrounds: A significant proportion of breast cancer patients showed receptor discordance between primary cancers and breast cancer brain metastases (BCBM), which significantly affected therapeutic decision-making. But it was not always feasible to obtain BCBM tissues. The aim of the present study was to analyze the receptor status of primary breast cancer and matched brain metastases and establish radiomic signatures to predict the receptor status of BCBM. Methods: The receptor status of 80 matched primary breast cancers and resected brain metastases were retrospectively analyzed. Radiomic features were extracted using preoperative brain MRI (contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2 fluid-attenuated inversion recovery, and combinations of these sequences) collected from 68 patients (45 and 23 for training and test sets, respectively) with BCBM excision. Using least absolute shrinkage selection operator and logistic regression model, the machine learning-based radiomic signatures were constructed to predict the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status of BCBM. Results: Discordance between the primary cancer and BCBM was found in 51.3% of patients, with 27.5%, 27.5%, and 5.0% discordance for ER, PR, and HER2, respectively. Loss of receptor expression was more common (33.8%) than gain (18.8%). The radiomic signatures built using combination sequences had the best performance in the training and test sets. The combination model yielded AUCs of 0.89, 0.88, and 0.87, classification sensitivities of 71.4%, 90%, and 87.5%, specificities of 81.2%, 76.9%, and 71.4%, and accuracies of 78.3%, 82.6%, and 82.6% for ER, PR, and HER2, respectively, in the test set. Conclusions: Receptor conversion in BCBM was common, and radiomic signatures show potential for noninvasively predicting BCBM receptor status.

Discriminating rectal cancer grades using restriction spectrum imaging.

PURPOSE: Restriction spectrum imaging (RSI) is a novel diffusion MRI model that separates water diffusion into several microscopic compartments. The restricted compartment correlating to the tumor cellularity is expected to be a potential indicator of rectal cancer aggressiveness. Our aim was to assess the ability of RSI model for rectal tumor grading. METHODS: Fifty-eight patients with different rectal cancer grading confirmed by biopsy were involved in this study. DWI acquisitions were performed using single-shot echo-planar imaging (SS-EPI) with multi-b-values at 3 T. We applied a three-compartment RSI model, along with ADC model and diffusion kurtosis imaging (DKI) model, to DWI images of 58 patients. ROC and AUC were used to compare the performance of the three models in differentiating the low grade (G1 + G2) and high grade (G3). Mean ± standard deviation, ANOVA, ROC analysis, and correlation analysis were used in this study. RESULTS: The volume fraction of restricted compartment C1 from RSI was significantly correlated with grades (r = 0.403, P = 0.002). It showed significant difference between G1 and G3 (P = 0.008) and between G2 and G3 (P = 0.01). As for the low-grade and high-grade discrimination, significant difference was found in C1 (P < 0.001). The AUC of C1 for differentiation between low-grade and high-grade groups was 0.753 with a sensitivity of 72.0% and a specificity of 70.0%. CONCLUSION: The three-compartment RSI model was able to discriminate the rectal cancer of low and high grades. The results outperform the traditional ADC model and DKI model in rectal cancer grading.

Tumor response evaluation by combined modalities of chest magnetic resonance imaging and computed tomography in locally advanced non-small cell lung cancer after concurrent chemoradiotherapy.

PURPOSE: This study aimed to explore the role of a modified criteria for assessing tumor response to concurrent chemoradiotherapy (CCRT) in locally advanced non-small cell lung cancer (LA-NSCLC), using the combined modalities of anatomical and functional MRI and CT. MATERIALS AND METHODS: One hundred and fifty-three patients with LA-NSCLC who underwent CCRT with continuous chest MRI and CT follow-up were analyzed. The tumor response to CCRT was evaluated two months after the completion of CCRT. The RECIST criteria (CT imaging) and modified criteria (combined chest MRI and CT imaging) were compared and validated on follow-up imaging. The chest MRI scan analysis included T1C, T2fs, DWI imaging and the apparent diffusion coefficient values. Progression free survival (PFS) ≥ 18 months was used as a surrogate endpoint of complete response to analyze the accuracy of tumor response assessment. RESULTS: Eight (5.2%) patients were considered to have complete response (CR) by the RECIST criteria while fifty-five (35.9%) were considered to have CR (CT + MRI) by the modified criteria. Using PFS ≥ 18 months as a surrogate for CR, the sensitivity and specificity of the modified criteria were 71.2% and 90.8% (AUC = 0.810, 95%CI 0.735-0.885), but were 9.1% and 97.7%, respectively, for the RECIST criteria (AUC = 0.534, 95%CI 0.441-0.627). The median PFS was 58.4 months for patients with CR (CT + MRI) and 9.7 months for those with non-CR (P < 0.001). Multivariate analysis showed that the tumor response evaluated by the modified criteria [CR (CT + MRI) vs. non-CR] was an independent factor for both PFS (HR 0.182, 95%CI 0.088-0.373, P < 0.001) and overall survival (HR 0.134, 95%CI 0.044-0.410, P < 0.001). CONCLUSIONS: Combined multi-parameter MRI and CT imaging could improve the accuracy of tumor response assessment in LA-NSCLC patients after definitive CCRT, therefore contributed to the risk stratification and survival prediction for clinical practice.

Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study.

BACKGROUND: Accurate detection is essential for brain metastasis (BM) management, but manual identification is laborious. This study developed, validated, and evaluated a BM detection (BMD) system. METHODS: Five hundred seventy-three consecutive patients (10 448 lesions) with newly diagnosed BMs and 377 patients without BMs were retrospectively enrolled to develop a multi-scale cascaded convolutional network using 3D-enhanced T1-weighted MR images. BMD was validated using a prospective validation set comprising an internal set (46 patients with 349 lesions; 44 patients without BMs) and three external sets (102 patients with 717 lesions; 108 patients without BMs). The lesion-based detection sensitivity and the number of false positives (FPs) per patient were analyzed. The detection sensitivity and reading time of three trainees and three experienced radiologists from three hospitals were evaluated using the validation set. RESULTS: The detection sensitivity and FPs were 95.8% and 0.39 in the test set, 96.0% and 0.27 in the internal validation set, and ranged from 88.9% to 95.5% and 0.29 to 0.66 in the external sets. The BMD system achieved higher detection sensitivity (93.2% [95% CI, 91.6-94.7%]) than all radiologists without BMD (ranging from 68.5% [95% CI, 65.7-71.3%] to 80.4% [95% CI, 78.0-82.8%], all P < .001). Radiologist detection sensitivity improved with BMD, reaching 92.7% to 95.0%. The mean reading time was reduced by 47% for trainees and 32% for experienced radiologists assisted by BMD relative to that without BMD. CONCLUSIONS: BMD enables accurate BM detection. Reading with BMD improves radiologists' detection sensitivity and reduces their reading times.

Prognostic Value of an Immunohistochemical Signature in Patients With Head and Neck Mucosal Melanoma.

Purpose: We aimed to develop a prognostic immunohistochemistry (IHC) signature for patients with head and neck mucosal melanoma (MMHN). Methods: In total, 190 patients with nonmetastatic MMHN with complete clinical and pathological data before treatment were included in our retrospective study. Results: We extracted five IHC markers associated with overall survival (OS) and then constructed a signature in the training set (n=116) with the least absolute shrinkage and selection operator (LASSO) regression model. The validation set (n=74) was further built to confirm the prognostic significance of this classifier. We then divided patients into high- and low-risk groups according to the IHC score. In the training set, the 5-year OS rate was 22.0% (95% confidence interval [CI]: 11.2%- 43.2%) for the high-risk group and 54.1% (95% CI: 41.8%-69.9%) for the low-risk group (P<0.001), and in the validation set, the 5-year OS rate was 38.1% (95% CI: 17.9%-81.1%) for the high-risk group and 43.1% (95% CI: 30.0%-61.9%) for the low-risk group (P=0.26). Multivariable analysis revealed that IHC score, T stage, and primary tumor site were independent variables for predicting OS (all P<0.05). We developed a nomogram incorporating clinicopathological risk factors (primary site and T stage) and the IHC score to predict 3-, 5-, and 10-year OS. Conclusions: A nomogram was generated and confirmed to be of clinical value. Our IHC classifier integrating five IHC markers could help clinicians make decisions and determine optimal treatments for patients with MMHN.

Dual-region radiomics signature: Integrating primary tumor and lymph node computed tomography features improves survival prediction in esophageal squamous cell cancer.

BACKGROUND: Preoperative prognostic biomarkers to guide individualized therapy are still in demand in esophageal squamous cell cancer (ESCC). Some studies reported that radiomic analysis based on CT images has been successfully performed to predict individual survival in EC. The aim of this study was to assess whether combining radiomics features from primary tumor and regional lymph nodes predicts overall survival (OS) better than using single-region features only, and to investigate the incremental value of the dual-region radiomics signature. METHODS: In this retrospective study, three radiomics signatures were built from preoperative enhanced CT in a training cohort (n = 200) using LASSO Cox model. Associations between each signature and survival was assessed on a validation cohort (n = 107). Prediction accuracy for the three signatures was compared. By constructing a clinical nomogram and a radiomics-clinical nomogram, incremental prognostic value of the radiomics signature over clinicopathological factors in OS prediction was assessed in terms of discrimination, calibration, reclassification and clinical usefulness. RESULTS: The dual-region radiomic signature was an independent factor, significantly associated with OS (HR: 1.869, 95% CI: 1.347, 2.592, P = 1.82e-04), which achieved better OS (C-index: 0.611) prediction either than the single-region signature (C-index:0.594-0.604). The resulted dual-region radiomics-clinical nomogram achieved the best discriminative ability in OS prediction (C-index:0.700). Compared with the clinical nomogram, the radiomics-clinical nomogram improved the calibration and classification accuracy for OS prediction with a total net reclassification improvement (NRI) of 26.9% (P=0.008) and integrated discrimination improvement (IDI) of 6.8% (P<0.001). CONCLUSION: The dual-region radiomic signature is an independent prognostic marker and outperforms single-region signature in OS for ESCC patients. Integrating the dual-region radiomics signature and clinicopathological factors improves OS prediction.

Radiomics Analysis of Susceptibility Weighted Imaging for Hepatocellular Carcinoma: Exploring the Correlation between Histopathology and Radiomics Features.

PURPOSE: No previous researches have extracted radiomics features from susceptibility weighted imaging (SWI) for biomedical applications. This research aimed to explore the correlation between histopathology of hepatocellular carcinoma (HCC) and radiomics features extracted from SWI. METHODS: A total of 53 patients were ultimately enrolled into this retrospective study with MR examinations undertaken at a 3T scanner. About 107 radiomics features were extracted from SWI images of each patient. Then, the Spearman correlation test was performed to evaluate the correlation between the SWI-derived radiomics features and histopathologic indexes including histopathologic grade, microvascular invasion (MVI) as well as the expression status of cytokeratin 7 (CK-7), cytokeratin 19 (CK-19) and Glypican-3 (GPC-3). With SWI-derived radiomics features utilized as independent variables, four logistic regression-based diagnostic models were established for diagnosing patients with positive CK-7, CK-19, GPC-3 and high histopathologic grade, respectively. Then, receiver operating characteristic analysis was performed to evaluate the diagnostic performance. RESULTS: A total of 11, 32, 18 and one SWI-derived radiomics features were significantly correlated with histopathologic grade, the expression of CK-7, the expression of CK-19 and the expression of GPC-3 (P < 0.05), respectively. None of the SWI-derived radiomics features was correlated with MVI status. The areas under the curve were 0.905, 0.837, 0.800 and 0.760 for diagnosing patients with positive CK-19, positive CK-7, high histopathologic grade and positive GPC-3. CONCLUSION: Extracting the radiomics features from SWI images was feasible to evaluate multiple histopathologic indexes of HCC.

Preoperatively Grading Rectal Cancer with the Combination of Intravoxel Incoherent Motions Imaging and Diffusion Kurtosis Imaging.

Purpose: To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer. Methods: A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance. Results: For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer (p < 0.05). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and K app (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer. Conclusion: The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity.

Evaluation of the Extent of Mesorectal Invasion and Mesorectal Fascia Involvement in Patients with T3 Rectal Cancer With 2-D and 3-D Transrectal Ultrasound: A Pilot Comparison Study With Magnetic Resonance Imaging Findings.

The aim of this study was to determine the value of 2-D and 3-D transrectal ultrasound (TRUS) in assessing the extent of mesorectal invasion (EMI) and mesorectal fascia involvement (MRF+) in patients with T3 rectal tumours. We retrospectively evaluated 80 patients with T3 stage rectal cancer who were pre-operatively evaluated by 2-D and 3-D TRUS before neoadjuvant chemoradiotherapy by using magnetic resonance imaging (MRI) as a reference standard. The T3 stage was subdivided into T3 ab (EMI ≤5 mm) and T3 cd (EMI >5 mm). The consistency assessment of the T3 sub-staging and MRF+ was compared between 2-D and 3-D TRUS using Cohen's kappa statistic. The concordance of the T3 sub-staging based on EMI was excellent between the 3-D TRUS and MRI (κ = 0.84) and good between the 2-D TRUS and MRI (κ = 0.67). For the assessment of MRF+ (κ = 0.82), 3-D TRUS and MRI showed excellent concordance. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 3-D TRUS for MRF+ assessment was 95.3%, 86.5%, 89.1% and 94.1%, respectively. The agreement between 3-D TRUS and MRI for the assessment of T3 sub-staging and MRF status was better in low rectal cancer (both κ = 0.85) than in middle (κ = 0.79 and 0.77) rectal cancer. Compared with MRI, 3-D TRUS has more advantages in the sub-staging of T3 rectal cancer and the assessment of MRF+ than those of 2-D TRUS, especially in low rectal cancer. For patients with T3 rectal cancer, 3-D TRUS may well complement MRI for selecting the appropriate treatment.

Correction to: A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.

The original version of this article, published on 21 February 2020, unfortunately contained a mistake.

A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.

OBJECTIVES: Classification of histologic subgroups has significant prognostic value for lung adenocarcinoma patients who undergo surgical resection. However, clinical histopathology assessment is generally performed on only a small portion of the overall tumor from biopsy or surgery. Our objective is to identify a noninvasive quantitative imaging biomarker (QIB) for the classification of histologic subgroups in lung adenocarcinoma patients. METHODS: We retrospectively collected and reviewed 1313 CT scans of patients with resected lung adenocarcinomas from two geographically distant institutions who were seen between January 2014 and October 2017. Three study cohorts, the training, internal validation, and external validation cohorts, were created, within which lung adenocarcinomas were divided into two disease-free-survival (DFS)-associated histologic subgroups, the mid/poor and good DFS groups. A comprehensive machine learning- and deep learning-based analytical system was adopted to identify reproducible QIBs and help to understand QIBs' significance. RESULTS: Intensity-Skewness, a QIB quantifying tumor density distribution, was identified as the optimal biomarker for predicting histologic subgroups. Intensity-Skewness achieved high AUCs (95% CI) of 0.849(0.813,0.881), 0.820(0.781,0.856) and 0.863(0.827,0.895) on the training, internal validation, and external validation cohorts, respectively. A criterion of Intensity-Skewness ≤ 1.5, which indicated high tumor density, showed high specificity of 96% (sensitivity 46%) and 99% (sensitivity 53%) on predicting the mid/poor DFS group in the training and external validation cohorts, respectively. CONCLUSIONS: A QIB derived from routinely acquired CT was able to predict lung adenocarcinoma histologic subgroups, providing a noninvasive method that could potentially benefit personalized treatment decision-making for lung cancer patients. KEY POINTS: • A noninvasive imaging biomarker, Intensity-Skewness, which described the distortion of pixel-intensity distribution within lesions on CT images, was identified as a biomarker to predict disease-free-survival-associated histologic subgroups in lung adenocarcinoma. • An Intensity-Skewness of ≤ 1.5 has high specificity in predicting the mid/poor disease-free survival histologic patient group in both the training cohort and the external validation cohort. • The Intensity-Skewness is a feature that can be automatically computed with high reproducibility and robustness.