Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery.

Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. We developed a laparoscopic real-time multispectral imaging system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional surgical view of the patient with functional information at a video rate of 25 Hz. To enable contrast agent-free ischemia monitoring during laparoscopic partial nephrectomy, we phrase the problem of ischemia detection as an out-of-distribution detection problem that does not rely on data from any other patient and uses an ensemble of invertible neural networks at its core. An in-human trial demonstrates the feasibility of our approach and highlights the potential of spectral imaging combined with advanced deep learning-based analysis tools for fast, efficient, reliable, and safe functional laparoscopic imaging.

Beyond rankings: Learning (more) from algorithm validation.

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond.

Bovine meat and milk factor protein expression in tumor-free mucosa of colorectal cancer patients coincides with macrophages and might interfere with patient survival.

Bovine milk and meat factors (BMMFs) are plasmid-like DNA molecules isolated from bovine milk and serum, as well as the peritumor of colorectal cancer (CRC) patients. BMMFs have been proposed as zoonotic infectious agents and drivers of indirect carcinogenesis of CRC, inducing chronic tissue inflammation, radical formation and increased levels of DNA damage. Data on expression of BMMFs in large clinical cohorts to test an association with co-markers and clinical parameters were not previously available and were therefore assessed in this study. Tissue sections with paired tumor-adjacent mucosa and tumor tissues of CRC patients [individual cohorts and tissue microarrays (TMAs) (n = 246)], low-/high-grade dysplasia (LGD/HGD) and mucosa of healthy donors were used for immunohistochemical quantification of the expression of BMMF replication protein (Rep) and CD68/CD163 (macrophages) by co-immunofluorescence microscopy and immunohistochemical scoring (TMA). Rep was expressed in the tumor-adjacent mucosa of 99% of CRC patients (TMA), was histologically associated with CD68+ /CD163+ macrophages and was increased in CRC patients when compared to healthy controls. Tumor tissues showed only low stromal Rep expression. Rep was expressed in LGD and less in HGD but was strongly expressed in LGD/HGD-adjacent tissues. Albeit not reaching statistical significance, incidence curves for CRC-specific death were increased for higher Rep expression (TMA), with high tumor-adjacent Rep expression being linked to the highest incidence of death. BMMF Rep expression might represent a marker and early risk factor for CRC. The correlation between Rep and CD68 expression supports a previous hypothesis that BMMF-specific inflammatory regulations, including macrophages, are involved in the pathogenesis of CRC.

Detection of Clinically Significant Prostate Cancer Using Targeted Biopsy with Four Cores Versus Target Saturation Biopsy with Nine Cores in Transperineal Prostate Fusion Biopsy: A Prospective Randomized Trial.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) and targeted biopsy (TB) facilitate accurate detection of clinically significant prostate cancer (csPC). However, it remains unclear how targeted cores should be applied for accurate diagnosis of csPC. OBJECTIVE: To assess csPC detection rates for two target-directed MRI/transrectal ultrasonography (TRUS) fusion biopsy approaches, conventional TB and target saturation biopsy (TS). DESIGN, SETTING, AND PARTICIPANTS: This was a prospective single-center study of outcomes for transperineal MRI/TRUS fusion biopsies for 170 men. Half of the men (n = 85) were randomized to conventional TB with four cores per lesion and half (n = 85) to TS with nine cores. Biopsies were performed by three experienced board-certified urologists. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: PC and csPC (International Society of Urological Pathology grade group ≥2) detection rates for systematic biopsy (SB), TB, and TS were analyzed using McNemar's test for intrapatient comparisons and Fisher's exact test for TS versus TB. A combination of targeted biopsy (TS or TB) and SB served as the reference. RESULTS AND LIMITATIONS: According to the reference, csPC was diagnosed for 57 men in the TS group and 36 men in the TB group. Of these, TS detected 57/57 csPC cases and TB detected 33/36 csPC cases (p = 0.058). Detection of Gleason grade group 1 disease was 10/12 cases with TS and 8/17 cases with TB (p = 0.055). In addition, TS detected 97% of 63 csPC lesions, compared to 86% with TB (p = 0.1). Limitations include the single-center design, the limited generalizability owing to the transperineal biopsy route, the lack of central review of pathology and radical prostatectomy correlation, and uneven distributions of csPC prevalence, Prostate Imaging-Reporting and Data System (PI-RADS) 5 lesions, men with two or more PI-RADS ≥3 lesions, and prostate-specific antigen density between the groups, which may have affected the results. CONCLUSIONS: In our study, rates of csPC detection did not significantly differ between TS and TB. PATIENT SUMMARY: In this study, we investigated two targeted approaches for taking prostate biopsy samples after observation of suspicious lesions on prostate scans. We found that the rates of detection of prostate cancer did not significantly differ between the two approaches.

Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model.

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method's current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

Three-dimensional Magnetic Resonance Imaging-based Printed Models of Prostate Anatomy and Targeted Biopsy-proven Index Tumor to Facilitate Patient-tailored Radical Prostatectomy-A Feasibility Study.

In this prospective single-center feasibility study, we demonstrate that the use of three-dimensional (3D)-printed prostate models support nerve-sparing radical prostatectomy (RP) and intraoperative frozen sectioning (IFS) in ten men suffering from intermediate- and high-risk prostate cancer (PC), of whom seven harbored pT3 disease. Patient-specific 3D resin models were printed based on preoperative multiparametric magnetic resonance imaging (mpMRI) to provide an exact 3D impression of significant tumor lesions. RP and IFS were planned in a patient-tailored fashion. The 36-region Prostate Imaging Reporting and Data System (PI-RADS) v2.0 scheme was used to compare the MRI/3D print with whole-mount histopathology. In all cases, localization of the index lesion was correctly displayed by MRI and the 3D model. Localization of significant PC lesions correlated significantly (Pearson`s correlation coefficient of 0.88; p < 0.001). In addition, a significant correlation of the width, length, and volume of the tumor and prostate gland, derived from the printed model and histopathology, was found, using Pearson's correlation analyses and Bland-Altman plots. In conclusion, 3D-printed prostate models correlate well with final pathology and can be used to tailor RP. PATIENT SUMMARY: The use of three-dimensional (3D)-printed prostate models based on preoperative magnetic resonance imaging (MRI) may improve prostatectomy outcome. This study confirmed the accuracy of 3D-printed prostates compared with pathology from radical prostatectomy specimens. Thus, MRI-derived 3D-printed prostate models can assist in prostate cancer surgery.

Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC.

Background Currently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previously developed quantitative RML and ADC classifiers for prediction of clinically significant prostate cancer (sPC) from prostate MRI, however these have not been combined with radiologist PI-RADS assessment. Purpose To propose and evaluate diagnostic algorithms combining quantitative ADC or RML and qualitative PI-RADS assessment for prediction of sPC. Methods and population The previously published quantitative models (RML and mADC) were utilized to construct four algorithms: 1) Down(ADC) and 2) Down(RML): clinically detected PI-RADS positive prostate lesions (defined as either PI-RADS≥3 or ≥4) were downgraded to MRI negative upon negative quantitative assessment; and 3) Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded to MRI-positive upon positive assessment of quantitative parameters. Analyses were performed at the individual lesion level and the patient level in 133 consecutive patients with suspicion for clinically significant prostate cancer (sPC, International Society of Urological Pathology (ISUP) grade group≥2), the test set subcohort of a previously published patient population. McNemar test was used to compare differences in sensitivity, specificity and accuracy. Differences between lesions of different prostate zones were assessed using ANOVA. Reduction in false positive assessments was assessed as ratios. Results Compared to clinical assessment at the PI-RADS≥4 cut-off alone, algorithms Down(ADC/RML) improved specificity from 43% to 65% (p = 0.001)/62% (p = 0.003), while sensitivity did not change significantly at 89% compared to 87% (p = 1.0)/89% (unchanged) on the patient level. Reduction of false positive lesions was 50% [26/52] in the PZ and 53% [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a patient basis, to an unfavorable loss of specificity from 43% to 30% (p = 0.039)/32% (p = 0.106), with insignificant increase of sensitivity from 89% to 96%/96% (both p = 1.0). Compared to clinical assessment at the PI-RADS≥3 cut-off alone, similar results were observed for Down(ADC) with significantly increased specificity from 2% to 23% (p < 0.001) and unchanged sensitivity on the lesion level; patient level specificity increased only non-significantly. Conclusion Downgrading PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC measurements or RML classifiers can increase diagnostic accuracy by enhancing specificity and preserving sensitivity for detection of sPC and reduce false positives.

Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge.

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.

OBJECTIVES: To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI. METHODS: In 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient. RESULTS: In the 259 eligible men (median 64 [IQR 61-72] years), PI-RADS had a sensitivity of 98% [106/108]/84% [91/108] with a specificity of 17% [25/151]/58% [88/151], for thresholds at ≥ 3/≥ 4 respectively. U-Net using dynamic threshold adjustment had a sensitivity of 99% [107/108]/83% [90/108] (p > 0.99/> 0.99) with a specificity of 24% [36/151]/55% [83/151] (p > 0.99/> 0.99) for probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and ≥ 4 decisions respectively, not statistically different from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4 examination and U-Net ≥ d3 assessment significantly improved the positive predictive value from 59 to 63% (p = 0.03), on a per-patient basis. CONCLUSIONS: U-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance. KEY POINTS: • U-Net maintained similar diagnostic performance compared to radiological assessment of PI-RADS ≥ 4 when applied in a simulated clinical deployment. • Application of our proposed prospective dynamic calibration method successfully adjusted U-Net performance within acceptable limits of the PI-RADS reference over time, while not being limited to PI-RADS as a reference. • Simultaneous detection by U-Net and radiological assessment significantly improved the positive predictive value on a per-patient and per-lesion basis, while the negative predictive value remained unchanged.

Detection of Significant Prostate Cancer Using Target Saturation in Transperineal Magnetic Resonance Imaging/Transrectal Ultrasonography-fusion Biopsy.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) and targeted biopsies (TBs) facilitate accurate detection of significant prostate cancer (sPC). However, it remains unclear how many cores should be applied per target. OBJECTIVE: To assess sPC detection rates of two different target-dependent magnetic resonance imaging (MRI)/transrectal ultrasonography (TRUS)-fusion biopsy approaches (TB and target saturation [TS]) compared with extended systematic biopsies (SBs). DESIGN, SETTING, AND PARTICIPANTS: Retrospective single-centre outcome of transperineal MRI/TRUS-fusion biopsies of 213 men was evaluated. All men underwent TB with a median of four cores per MRI lesion, followed by a median of 24 SBs, performed by experienced urologists. Cancer and sPC (International Society of Urological Pathology grade group ≥2) detection rates were analysed. TB was compared with SB and TS, with nine cores per target, calculated by the Ginsburg scheme and using individual cores of the lesion and its "penumbra". OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Cancer detection rates were calculated for TS, TB, and SB at both lesion and patient level. Combination of SB + TB served as a reference. Statistical differences in prostate cancer (PC) detection between groups were calculated using McNemar's tests with confidence intervals. RESULTS AND LIMITATIONS: TS detected 99% of 134 sPC lesions, which was significantly higher than the detection by TB (87%, p = 0.001) and SB (82%, p < 0.001). SB detected significantly more of the 72 low-risk PC lesions than TB (99% vs 68%, p < 0.001) and 10% (p = 0.15) more than that detected by TS. At a per-patient level, 99% of men harbouring sPC were detected by TS. This was significantly higher than that by TB and SB (89%, p = 0.03 and 81%, p = 0.001, respectively). Limitations include limited generalisability, as a transperineal biopsy route was used. CONCLUSIONS: TS detected significantly more cases of sPC than TB and extended SB. Given that both 99% of sPC lesions and men harbouring sPC were identified by TS, the results suggest that this approach allows to omit SB cores without compromising sPC detection. PATIENT SUMMARY: Target saturation of magnetic resonance imaging-suspicious prostate lesions provides excellent cancer detection and finds fewer low-risk tumours than the current gold standard combination of targeted and systematic biopsies.

External validation of novel magnetic resonance imaging-based models for prostate cancer prediction.

OBJECTIVES: To validate, in an external cohort, three novel risk models, including the recently updated European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator, that combine multiparametric magnetic resonance imaging (mpMRI) and clinical variables to predict clinically significant prostate cancer (PCa). PATIENTS AND METHODS: We retrospectively analysed 307 men who underwent mpMRI prior to transperineal ultrasound fusion biopsy between October 2015 and July 2018 at two German centres. mpMRI was rated by Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and clinically significant PCa was defined as International Society of Urological Pathology Gleason grade group ≥2. The prediction performance of the three models (MRI-ERSPC-3/4, and two risk models published by Radtke et al. and Distler et al., ModRad and ModDis) were compared using receiver-operating characteristic (ROC) curve analyses, with area under the ROC curve (AUC), calibration curve analyses and decision curves used to assess net benefit. RESULTS: The AUCs of the three novel models (MRI-ERSPC-3/4, ModRad and ModDis) were 0.82, 0.85 and 0.83, respectively. Calibration curve analyses showed the best intercept for MRI-ERSPC-3 and -4 of 0.35 and 0.76. Net benefit analyses indicated clear benefit of the MRI-ERSPC-3/4 risk models compared with the other two validated models. The MRI-ERSPC-3/4 risk models demonstrated a discrimination benefit for a risk threshold of up to 15% for clinically significant PCa as compared to the other risk models. CONCLUSION: In our external validation of three novel prostate cancer risk models, which incorporate mpMRI findings, a head-to-head comparison indicated that the MRI-ERSPC-3/4 risk model in particular could help to reduce unnecessary biopsies.

Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for the Prediction of Extraprostatic Disease-A Risk Model for Patient-tailored Risk Stratification When Planning Radical Prostatectomy.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) facilitates the detection of significant prostate cancer. Therefore, addition of mpMRI to clinical parameters might improve the prediction of extraprostatic extension (EPE) in radical prostatectomy (RP) specimens. OBJECTIVE: To investigate the accuracy of a novel risk model (RM) combining clinical and mpMRI parameters to predict EPE in RP specimens. DESIGN, SETTING, AND PARTICIPANTS: We added prebiopsy mpMRI to clinical parameters and developed an RM to predict individual side-specific EPE (EPE-RM). Clinical parameters of 264 consecutive men with mpMRI prior to MRI/transrectal ultrasound fusion biopsy and subsequent RP between 2012 and 2015 were retrospectively analysed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariate regression analyses were used to determine significant EPE predictors for RM development. The prediction performance of the novel EPE-RM was compared with clinical T stage (cT), MR-European Society of Urogenital Radiology (ESUR) classification for EPE, two established nomograms (by Steuber et al and Ohori et al) and a clinical nomogram based on the coefficients of the established nomograms, and was constructed based on the data of the present cohort, using receiver operating characteristics (ROCs). For comparison, models' likelihood ratio (LR) tests and Vuong tests were used. Discrimination and calibration of the EPE-RM were validated based on resampling methods using bootstrapping. RESULTS AND LIMITATIONS: International society of Urogenital Pathology grade on biopsy, ESUR criteria, prostate-specific antigen, cT, prostate volume, and capsule contact length were included in the EPE-RM. Calibration of the EPE-RM was good (error 0.018). The ROC area under the curve for the EPE-RM was larger (0.87) compared with cT (0.66), Memorial Sloan Kettering Cancer Center nomogram (0.73), Steuber nomogram (0.70), novel clinical nomogram (0.79), and ESUR classification (0.81). Based on LR and Vuong tests, the EPE-RM's model fit was significantly better than that of cT, all clinical models, and ESUR classification alone (p<0.001). Limitations include monocentric design and expert reading of MRI. CONCLUSIONS: This novel EPE-RM, incorporating clinical and MRI parameters, performed better than contemporary clinical RMs and MRI predictors, therefore providing an accurate patient-tailored preoperative risk stratification of side-specific EPE. PATIENT SUMMARY: Extraprostatic extension of prostate cancer can be predicted accurately using a combination of magnetic resonance imaging and clinical parameters. This novel risk model outperforms magnetic resonance imaging and clinical predictors alone and can be useful when planning nerve-sparing radical prostatectomy.

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.

Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose To compare the performance of clinical assessment to a deep learning system optimized for segmentation trained with T2-weighted and diffusion MRI in the task of detection and segmentation of lesions suspicious for sPC. Materials and Methods In this retrospective study, T2-weighted and diffusion prostate MRI sequences from consecutive men examined with a single 3.0-T MRI system between 2015 and 2016 were manually segmented. Ground truth was provided by combined targeted and extended systematic MRI-transrectal US fusion biopsy, with sPC defined as International Society of Urological Pathology Gleason grade group greater than or equal to 2. By using split-sample validation, U-Net was internally validated on the training set (80% of the data) through cross validation and subsequently externally validated on the test set (20% of the data). U-Net-derived sPC probability maps were calibrated by matching sextant-based cross-validation performance to clinical performance of Prostate Imaging Reporting and Data System (PI-RADS). Performance of PI-RADS and U-Net were compared by using sensitivities, specificities, predictive values, and Dice coefficient. Results A total of 312 men (median age, 64 years; interquartile range [IQR], 58-71 years) were evaluated. The training set consisted of 250 men (median age, 64 years; IQR, 58-71 years) and the test set of 62 men (median age, 64 years; IQR, 60-69 years). In the test set, PI-RADS cutoffs greater than or equal to 3 versus cutoffs greater than or equal to 4 on a per-patient basis had sensitivity of 96% (25 of 26) versus 88% (23 of 26) at specificity of 22% (eight of 36) versus 50% (18 of 36). U-Net at probability thresholds of greater than or equal to 0.22 versus greater than or equal to 0.33 had sensitivity of 96% (25 of 26) versus 92% (24 of 26) (both P > .99) with specificity of 31% (11 of 36) versus 47% (17 of 36) (both P > .99), not statistically different from PI-RADS. Dice coefficients were 0.89 for prostate and 0.35 for MRI lesion segmentation. In the test set, coincidence of PI-RADS greater than or equal to 4 with U-Net lesions improved the positive predictive value from 48% (28 of 58) to 67% (24 of 36) for U-Net probability thresholds greater than or equal to 0.33 (P = .01), while the negative predictive value remained unchanged (83% [25 of 30] vs 83% [43 of 52]; P > .99). Conclusion U-Net trained with T2-weighted and diffusion MRI achieves similar performance to clinical Prostate Imaging Reporting and Data System assessment. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Padhani and Turkbey in this issue.

Prediction of significant prostate cancer in biopsy-naïve men: Validation of a novel risk model combining MRI and clinical parameters and comparison to an ERSPC risk calculator and PI-RADS.

BACKGROUND: Risk models (RM) need external validation to assess their value beyond the setting in which they were developed. We validated a RM combining mpMRI and clinical parameters for the probability of harboring significant prostate cancer (sPC, Gleason Score ≥ 3+4) for biopsy-naïve men. MATERIAL AND METHODS: The original RM was based on data of 670 biopsy-naïve men from Heidelberg University Hospital who underwent mpMRI with PI-RADS scoring prior to MRI/TRUS-fusion biopsy 2012-2015. Validity was tested by a consecutive cohort of biopsy-naïve men from Heidelberg (n = 160) and externally by a cohort of 133 men from University College London Hospital (UCLH). Assessment of validity was performed at fusion-biopsy by calibration plots, receiver operating characteristics curve and decision curve analyses. The RM`s performance was compared to ERSPC-RC3, ERSPC-RC3+PI-RADSv1.0 and PI-RADSv1.0 alone. RESULTS: SPC was detected in 76 men (48%) at Heidelberg and 38 men (29%) at UCLH. The areas under the curve (AUC) were 0.86 for the RM in both cohorts. For ERSPC-RC3+PI-RADSv1.0 the AUC was 0.84 in Heidelberg and 0.82 at UCLH, for ERSPC-RC3 0.76 at Heidelberg and 0.77 at UCLH and for PI-RADSv1.0 0.79 in Heidelberg and 0.82 at UCLH. Calibration curves suggest that prevalence of sPC needs to be adjusted to local circumstances, as the RM overestimated the risk of harboring sPC in the UCLH cohort. After prevalence-adjustment with respect to the prevalence underlying ERSPC-RC3 to ensure a generalizable comparison, not only between the Heidelberg and die UCLH subgroup, the RM`s Net benefit was superior over the ERSPC`s and the mpMRI`s for threshold probabilities above 0.1 in both cohorts. CONCLUSIONS: The RM discriminated well between men with and without sPC at initial MRI-targeted biopsy but overestimated the sPC-risk at UCLH. Taking prevalence into account, the model demonstrated benefit compared with clinical risk calculators and PI-RADSv1.0 in making the decision to biopsy men at suspicion of PC. However, prevalence differences must be taken into account when using or validating the presented risk model.

Histopathological to multiparametric MRI spatial mapping of extended systematic sextant and MR/TRUS-fusion-targeted biopsy of the prostate.

PURPOSE: MRI has limited ability to detect multifocal disease or the full extent of prostate involvement with clinically significant prostate cancer (sPC). We compare the spatial co-localization at sextant resolution of MRI lesions and histopathological mapping by combined targeted and extended systematic biopsies. MATERIALS AND METHODS: Sextants were mapped for sPC (ISUP group ≥ 2) by 24-core transperineal systematic biopsies in 316 patients with suspicion for sPC and by MR lesions of PI-RADS score of ≥ 3. The gold standard is combined systematic (median 23 cores) and targeted biopsies. RESULTS: Of 316 men, 121 (38%) harbored sPC. Of these 121 patients, 4 (3%) had a negative MRI. MRI correctly identified 117/121 (97%) patients with sPC. In these patients, mpMRI missed no additional sPC in 96 (82%), while MRI-negative sPC lesions were present in 21 patients (18%). Of 1896 sextants, 379 (20%) harbored sPC. MR-positive sextants contained sPC in 26% (337/1275), compared to 7% (42/621) in MR-negative sextants. On a patient basis, sensitivity was 0.97, specificity 0.22, positive predictive value 0.43, and negative predictive value 0.91. On a sextant basis, sensitivity was 0.73, specificity 0.38, positive predictive value 0.26, and negative predictive value 0.93. CONCLUSION: MpMRI mapping agreed well with histopathology with, at the observed sPC prevalence and on a patient basis, excellent sensitivity and negative predictive value, and acceptable specificity and positive predictive value for sPC. However, 18% of sPC was outside the mpMRI mapped region, quantifying limitations of MRI for complete localization of disease extent. KEY POINTS: • Currently, exclusive MRI mapping of the prostate for focal treatment planning cannot be recommended, as significant prostate cancer may remain untreated in a substantial number of cases. • At the observed sPC prevalence and on a patient basis, mpMRI has excellent sensitivity and NPV, and acceptable specificity and PPV for detection of prostate cancer, supporting its use to detect suspicious lesions before biopsy. • Despite the excellent global performance, 18% of sPC was outside the mpMRI mapped region even when a security margin of 10 mm was considered, indicating that prostate MRI has limited ability to completely map all cancer foci within the prostate.

Correlation between genomic index lesions and mpMRI and 68Ga-PSMA-PET/CT imaging features in primary prostate cancer.

Magnetic resonance imaging (MRI) and prostate specific membrane antigen (PSMA)- positron emission tomography (PET)/computed tomography (CT)-imaging of prostate cancer (PCa) are emerging techniques to assess the presence of significant disease and tumor progression. It is not known, however, whether and to what extent lesions detected by these imaging techniques correlate with genomic features of PCa. The aim of this study was therefore to define a genomic index lesion based on chromosomal copy number alterations (CNAs) as marker for tumor aggressiveness in prostate biopsies in direct correlation to multiparametric (mp) MRI and 68Ga-PSMA-PET/CT imaging features. CNA profiles of 46 biopsies from five consecutive patients with clinically high-risk PCa were obtained from radiologically suspicious and unsuspicious areas. All patients underwent mpMRI, MRI/TRUS-fusion biopsy, 68Ga-PSMA-PET/CT and a radical prostatectomy. CNAs were directly correlated to imaging features and radiogenomic analyses were performed. Highly significant CNAs (≥10 Mbp) were found in 22 of 46 biopsies. Chromosome 8p, 13q and 5q losses were the most common findings. There was an strong correspondence between the radiologic and the genomic index lesions. The radiogenomic analyses suggest the feasibility of developing radiologic signatures that can distinguish between genomically more or less aggressive lesions. In conclusion, imaging features of mpMRI and 68Ga-PSMA-PET/CT can guide to the genomically most aggressive lesion of a PCa. Radiogenomics may help to better differentiate between indolent and aggressive PCa in the future.

Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values.

Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials and Methods This single-institution study included 316 men (mean age ± standard deviation, 64.0 years ± 7.8) with an indication for MRI-transrectal US fusion biopsy between May 2015 and September 2016 (training cohort, 183 patients; test cohort, 133 patients). Lesions identified by prospective clinical readings were manually segmented for mean ADC and radiomics analysis. Global and zone-specific random forest RML and mean ADC models for classification of clinically significant prostate cancer (Gleason grade group ≥ 2) were developed on the training set and the fixed models tested on an independent test set. Clinical readings, mean ADC, and radiomics were compared by using the McNemar test and receiver operating characteristic (ROC) analysis. Results In the test set, radiologist interpretation had a per-lesion sensitivity of 88% (53 of 60) and specificity of 50% (79 of 159). Quantitative measurement of the mean ADC (cut-off 732 mm2/sec) significantly reduced false-positive (FP) lesions from 80 to 60 (specificity 62% [99 of 159]) and false-negative (FN) lesions from seven to six (sensitivity 90% [54 of 60]) (P = .048). Radiologist interpretation had a per-patient sensitivity of 89% (40 of 45) and specificity of 43% (38 of 88). Quantitative measurement of the mean ADC reduced the number of patients with FP lesions from 50 to 43 (specificity 51% [45 of 88]) and the number of patients with FN lesions from five to three (sensitivity 93% [42 of 45]) (P = .496). Comparison of the area under the ROC curve (AUC) for the mean ADC (AUCglobal = 0.84; AUCzone-specific ≤ 0.87) vs the RML (AUCglobal = 0.88, P = .176; AUCzone-specific ≤ 0.89, P ≥ .493) showed no significantly different performance. Conclusion Quantitative measurement of the mean apparent diffusion coefficient (ADC) improved differentiation of benign versus malignant prostate lesions, compared with clinical assessment. Radiomic machine learning had comparable but not better performance than mean ADC assessment. © RSNA, 2018 Online supplemental material is available for this article.

Exploiting the potential of unlabeled endoscopic video data with self-supervised learning.

PURPOSE: Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field. The purpose of this paper was to investigate the concept of self-supervised learning to address this issue. METHODS: Our approach is guided by the hypothesis that unlabeled video data can be used to learn a representation of the target domain that boosts the performance of state-of-the-art machine learning algorithms when used for pre-training. Core of the method is an auxiliary task based on raw endoscopic video data of the target domain that is used to initialize the convolutional neural network (CNN) for the target task. In this paper, we propose the re-colorization of medical images with a conditional generative adversarial network (cGAN)-based architecture as auxiliary task. A variant of the method involves a second pre-training step based on labeled data for the target task from a related domain. We validate both variants using medical instrument segmentation as target task. RESULTS: The proposed approach can be used to radically reduce the manual annotation effort involved in training CNNs. Compared to the baseline approach of generating annotated data from scratch, our method decreases exploratively the number of labeled images by up to 75% without sacrificing performance. Our method also outperforms alternative methods for CNN pre-training, such as pre-training on publicly available non-medical (COCO) or medical data (MICCAI EndoVis2017 challenge) using the target task (in this instance: segmentation). CONCLUSION: As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training.

Antibodies against human papillomaviruses as diagnostic and prognostic biomarker in patients with neck squamous cell carcinoma from unknown primary tumor.

Treatment of patients with neck lymph node metastasis of squamous cell carcinoma (SCC) from unknown primary tumor (NSCCUP) is challenging due to the risk of missing occult tumors or inducing toxicity to unaffected sites. Human papillomavirus (HPV) is a promising biomarker given its causal link to oropharyngeal SCC and superior survival of patients with HPV-driven oropharyngeal SCC and NSCCUP. Identification of HPV-driven NSCCUP could focus diagnostic work-up and treatment on the oropharynx. For the first time, we assessed HPV antibodies and their prognostic value in NSCCUP patients. Antibodies against E6 and E7 (HPV16/18/31/33/35), E1 and E2 (HPV16/18) were assessed in 46 NSCCUP patients in sera collected at diagnosis, and in follow-up sera from five patients. In 28 patients, HPV tumor status was determined using molecular markers (HPV DNA, mRNA and cellular p16INK4a ). Thirteen (28%) NSCCUP patients were HPV-seropositive for HPV16, 18, 31, or 33. Of eleven patients with HPV-driven NSCCUP, ten were HPV-seropositive, while all 17 patients with non-HPV-driven NSCCUP were HPV-seronegative, resulting in 91% sensitivity (95% CI: 59-100%) and 100% specificity (95% CI: 80-100%). HPV antibody levels decreased after curative treatment. Recurrence was associated with increasing levels in an individual case. HPV-seropositive patients had a better overall and progression-free survival with hazard ratios of 0.09 (95% CI: 0.01-0.42) and 0.03 (95% CI: 0.002-0.18), respectively. For the first time, seropositivity to HPV proteins is described in NSCCUP patients, and high sensitivity and specificity for HPV-driven NSCCUP are demonstrated. HPV seropositivity appears to be a reliable diagnostic and prognostic biomarker for patients with HPV-driven NSCCUP.