A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists.

OBJECTIVES: To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. METHODS: In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. RESULTS: In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). CONCLUSIONS: DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. KEY POINTS: • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade.

Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in Patients With Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Fibroblast activation protein (FAP) in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment of patients. Accurate preoperative FAP expression can better identify the population benefitting from FAP-targeting drugs. PURPOSE: To develop and validate a machine learning classifier based on noncontrast MRI for the preoperative prediction of FAP expression in patients with PDAC. STUDY TYPE: Retrospective cohort study. POPULATION: Altogether, 129 patients with pathology-confirmed PDAC undergoing MR scan and surgical resection; 90 patients in a training cohort, and 39 patients in a validation cohort. FIELD STRENGTH/SEQUENCE/3T: Breath-hold single-shot fast-spin echo T2-weighted sequence and unenhanced and noncontrast T1-weighted fat-suppressed sequences. ASSESSMENT: FAP expression was quantified using immunohistochemistry. For each patient, 1409 radiomics features were extracted from T1- and T2-weighted images and reduced using the least absolute shrinkage and selection operator logistic regression algorithm. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. The MLP network classifier performance was determined by its discriminative ability, calibration, and clinical utility. STATISTICAL TESTS: Kaplan-Meier estimates, student's t-test, the Kruskal-Wallis H test, and the chi-square test, univariable regression analysis, receiver operating characteristic curve, and decision curve analysis were used. RESULTS: A log-rank test showed that the survival of patients with low FAP expression (24.43 months) was significantly longer (P < 0.05) than that in the FAP-high group (13.50 months). The prediction model showed good discrimination in the training set (area under the curve [AUC], 0.84) and the validation set (AUC, 0.77). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 75.00%, 79.41%, 0.77, 0.86, and 0.66, respectively, whereas those for the validation set were 85.00%, 63.16%, 0.74, 0.71, and 0.80, respectively. DATA CONCLUSIONS: The MLP network classifier based on noncontrast MRI can accurately predict FAP expression in patients with PDAC. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

MRI-based radiomics approach for differentiation of hypovascular non-functional pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms of the pancreas.

BACKGROUND: This study aims to investigate the value of radiomics parameters derived from contrast enhanced (CE) MRI in differentiation of hypovascular non-functional pancreatic neuroendocrine tumors (hypo-NF-pNETs) and solid pseudopapillary neoplasms of the pancreas (SPNs). METHODS: Fifty-seven SPN patients and twenty-two hypo-NF-pNET patients were enrolled. Radiomics features were extracted from T1WI, arterial, portal and delayed phase of MR images. The enrolled patients were divided into training cohort and validation cohort with the 7:3 ratio. We built four radiomics signatures for the four phases respectively and ROC analysis were used to select the best phase to discriminate SPNs from hypo-NF-pNETs. The chosen radiomics signature and clinical independent risk factors were integrated to construct a clinic-radiomics nomogram. RESULTS: SPNs occurred in younger age groups than hypo-NF-pNETs (P < 0.0001) and showed a clear preponderance in females (P = 0.0185). Age was a significant independent factor for the differentiation of SPNs and hypo-NF-pNETs revealed by logistic regression analysis. With AUC values above 0.900 in both training and validation cohort (0.978 [95% CI, 0.942-1.000] in the training set, 0.907 [95% CI, 0.765-1.000] in the validation set), the radiomics signature of the arterial phase was picked to build a clinic-radiomics nomogram. The nomogram, composed by age and radiomics signature of the arterial phase, showed sufficient performance for discriminating SPNs and hypo-NF-pNETs with AUC values of 0.965 (95% CI, 0.923-1.000) and 0.920 (95% CI, 0.796-1.000) in the training and validation cohorts, respectively. Delong Test did not demonstrate statistical significance between the AUC of the clinic-radiomics nomogram and radiomics signature of arterial phase. CONCLUSION: CE-MRI-based radiomics approach demonstrated great potential in the differentiation of hypo-NF-pNETs and SPNs.

Esophageal squamous cell carcinoma metastases to kidney and renal hilar lymph nodes through epithelial-mesenchymal transition: a case report and literature review.

BACKGROUND: Esophageal cancer (EC) metastasized to the kidney is extremely rare clinically. Here, we present a case of metachronous renal metastasis of esophageal squamous cell carcinoma (ESCC) through epithelial-mesenchymal transition (EMT). CASE PRESENTATION: A 60-year-old patient, male, complained of left waist pain for 5 days, 11 months after radical esophagectomy. Laboratory tests revealed haematuria. Both CT and PET-CT scan showed retroperitoneal lymph nodes and left renal masses. Subsequently the patient received a left nephrectomy and lymph nodes resection, and squamous cell carcinoma of kidney and renal hilar lymph nodes was diagnosed, combined with morphology, medical history and immunophenotype, it was presumed to be metastasis of ESCC through the EMT pathway. CONCLUSIONS: The renal metastasis of squamous cell carcinoma should be considered in patients with history of EC, although this is very rare. Histopathological examination combined with immunochemical detection is helpful in differential diagnosis.

Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique.

BACKGROUND: Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency. METHODS: This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant. RESULTS: DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD. CONCLUSIONS: Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. TRIAL REGISTRATION: ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.

Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram.

Objective: To develop and validate a multimodal MRI-based radiomics nomogram for predicting clinically significant prostate cancer (CS-PCa). Methods: Patients who underwent radical prostatectomy with pre-biopsy prostate MRI in three different centers were assessed retrospectively. Totally 141 and 60 cases were included in the training and test sets in cohort 1, respectively. Then, 66 and 122 cases were enrolled in cohorts 2 and 3, as external validation sets 1 and 2, respectively. Two different manual segmentation methods were established, including lesion segmentation and whole prostate segmentation on T2WI and DWI scans, respectively. Radiomics features were obtained from the different segmentation methods and selected to construct a radiomics signature. The final nomogram was employed for assessing CS-PCa, combining radiomics signature and PI-RADS. Diagnostic performance was determined by receiver operating characteristic (ROC) curve analysis, net reclassification improvement (NRI) and decision curve analysis (DCA). Results: Ten features associated with CS-PCa were selected from the model integrating whole prostate (T2WI) + lesion (DWI) for radiomics signature development. The nomogram that combined the radiomics signature with PI-RADS outperformed the subjective evaluation alone according to ROC analysis in all datasets (all p<0.05). NRI and DCA confirmed that the developed nomogram had an improved performance in predicting CS-PCa. Conclusions: The established nomogram combining a biparametric MRI-based radiomics signature and PI-RADS could be utilized for noninvasive and accurate prediction of CS-PCa.

Knockdown of lncRNA MIR4435­2HG and ST8SIA1 expression inhibits the proliferation, invasion and migration of prostate cancer cells in vitro and in vivo by blocking the activation of the FAK/AKT/ß­catenin signaling pathway.

Prostate cancer is a main health risk for males with a high incidence and mortality. The present study aimed to examine the effects of long non­coding RNA (lncRNA) MIR4435­2HG binding with ST8SIA1 on the proliferation, invasion and migration of prostate cancer cells via the activation of the FAK/AKT/ß­catenin signaling pathway. The expression of MIR4435­2HG and ST8SIA1 in prostate cancer cell lines, and the transfection efficacy were analyzed by RT­qPCR. The proliferation, clone formation ability, and the invasion and migration of transfected cells were detected by CCK­8 assay, clone formation assay, Transwell assay and wound healing assay, respectively. Plasmids were injected subcutaneously into mice to construct a xenograft tumor model. The expression levels of proteins related to proliferation, apoptosis, invasion and migration, and the FAK/AKT/ß­catenin pathway were detected by western blot analysis. The results revealed that MIR4435­2HG expression was increased in the prostate cancer cell lines and MIR4435­2HG expression was the highest in the PC­3 cells. Interference with MIR4435­2HG inhibited the proliferation, clone formation ability, and the invasion and migration of PC­3 cells, as well as tumor growth by suppressing the activation of the FAK/AKT/ß­catenin signaling pathway. MIR4435­2HG was demonstrated to target ST8SIA1. ST8SIA1 expression was also increased in the prostate cancer cell lines and MIR4435­2HG expression was the highest in the PC­3 cells. Interference with ST8SIA1 inhibited the promoting effects of MIR4435­2HG on the proliferation, invasion and migration of PC­3 cells, as well as tumor growth by suppressing the activation of the FAK/AKT/ß­catenin signaling pathway. On the whole, the present study demonstrates that interference with MIR4435­2HG, combined with ST8SIA1, inhibits the proliferation, invasion and migration of prostate cancer cells in vitro and in vivo by blocking the activation of the FAK/AKT/ß­catenin signaling pathway.

Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging.

BACKGROUND: To explore the usefulness of analyzing histograms and textures of apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images to differentiate prostatic cancer (PCa) from benign prostatic hyperplasia (BPH) using histopathology as the reference. METHODS: Ninety patients with PCa and 112 patients with BPH were included in this retrospective study. Differences in whole-lesion histograms and texture parameters of ADC maps and T2W images between PCa and BPH patients were evaluated using the independent samples t-test. The diagnostic performance of ADC maps and T2W images in being able to differentiate PCa from BPH was assessed using receiver operating characteristic (ROC) curves. RESULTS : The mean, median, 5th, and 95th percentiles of ADC values in images from PCa patients were significantly lower than those from BPH patients (p < 0.05). Significant differences were observed in the means, standard deviations, medians, kurtosis, skewness, and 5th percentile values of T2W image between PCa and BPH patients (p < 0.05). The ADC5th showed the largest AUC (0.906) with a sensitivity of 83.3 % and specificity of 89.3 %. The diagnostic performance of the T2W image histogram and texture analysis was moderate and had the largest AUC of 0.634 for T2WKurtosis with a sensitivity and specificity of 48.9% and 79.5 %, respectively. The diagnostic performance of the combined ADC5th & T2WKurtosis parameters was also similar to that of the ADC5th & ADCDiff-Variance. CONCLUSIONS: Histogram and texture parameters derived from the ADC maps and T2W images for entire prostatic lesions could be used as imaging biomarkers to differentiate PCa and BPH biologic characteristics, however, histogram parameters outperformed texture parameters in the diagnostic performance.

Single-Stage Pulmonary Resection via a Combination of Single Hookwire Localization and Video-Assisted Thoracoscopic Surgery for Synchronous Multiple Pulmonary Nodules.

Purpose: To retrospectively analyze the incidence and predictors of complications related to hookwire localization in patients with single and multiple nodules, and to evaluate the usefulness of a single-stage surgical method of single hookwire localization combined with video-assisted thoracoscopic surgery (VATS) in synchronous multiple pulmonary nodules (SMPNs). Methods: A total of 200 patients who underwent computed tomography (CT)-guided hookwire localization and subsequent VATS resection were enrolled in this study. For each patient, only 1 indeterminate nodule was implanted with a hookwire. There were 145 patients in the single-nodule group (Group S) and 55 in the multiple-nodule group (Group M). Univariate and binary logistic regression analyses were used to assess incidence and predictors of complications associated with hookwire localization. Results: The technical success rate of hookwire implantation was 97.5%. The incidence of pneumothorax and hookwire dislodgement was 17.0% and 2.5%, respectively. Binary logistic regression analysis showed that 1 transpleural puncture through the pleura (odds ratio [OR] = 0.433, P = .033) was the only independent protective factor for pneumothorax, and pneumothorax (OR = 26.114, P < .01) was the only independent risk factor for dislodgement. The volume of blood loss during VATS was significantly higher in group M than in group S, and the time of postoperative hospitalization was significantly longer in group M than in group S. About 44 patients in group M with additional 58 nodules without localization had undergone direct surgical resection simultaneously, and bilateral surgery was performed in 13 patients (29.5%). The intrathoracic recurrence rate was 4.8% during follow-up CT. Conclusion: Single-stage surgery via an approach of single hookwire localization combined with VATS is feasible and safe for SMPNs.

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.

OBJECTIVE: The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. MATERIALS AND METHODS: We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated. RESULTS: The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001). CONCLUSIONS: Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.

Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience.

PURPOSE: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI. METHODS: This retrospective study included 121 patients who underwent prebiopsy MRI and prostate biopsy. More than five radiologists (Reader groups 1, 2: residents; Readers 3, 4: less-experienced radiologists; Reader 5: expert) independently reviewed biparametric MRI (bpMRI). The DLA results were obtained using bpMRI. The reference standard was based on pathologic reports. The diagnostic performance of the PI-RADS classification of DLA, clinical reports, and radiologists was analyzed using AUROC. Dichotomous analysis (PI-RADS cutoff value ≥ 3 or 4) was performed, and the sensitivities and specificities were compared using McNemar's test. RESULTS: Clinically significant cancer [CSC, Gleason score ≥ 7] was confirmed in 43 patients (35.5%). The AUROC of the DLA (0.828) for diagnosing CSC was significantly higher than that of Reader 1 (AUROC, 0.706; p = 0.011), significantly lower than that of Reader 5 (AUROC, 0.914; p = 0.013), and similar to clinical reports and other readers (p = 0.060-0.661). The sensitivity of DLA (76.7%) was comparable to those of all readers and the clinical reports at a PI-RADS cutoff value ≥ 4. The specificity of the DLA (85.9%) was significantly higher than those of clinical reports and Readers 2-3 and comparable to all others at a PI-RADS cutoff value ≥ 4. CONCLUSIONS: The DLA showed moderate diagnostic performance at a level between those of residents and an expert in detecting and classifying according to PI-RADS. The performance of DLA was similar to that of clinical reports from various radiologists in clinical practice.