Fractionated stereotactic radiotherapy of intracranial postoperative cavities after resection of brain metastases - Clinical outcome and prognostic factors.

Background and Purpose: After surgical resection of brain metastases (BM), radiotherapy (RT) is indicated. Postoperative stereotactic radiosurgery (SRS) reduces the risk of local progression and neurocognitive decline compared to whole brain radiotherapy (WBRT). Aside from the optimal dose and fractionation, little is known about the combination of systemic therapy and postoperative fractionated stereotactic radiotherapy (fSRT), especially regarding tumour control and toxicity. Methods: In this study, 105 patients receiving postoperative fSRT with 35 Gy in 7 fractions performed with Cyberknife were retrospectively reviewed. Overall survival (OS), local control (LC) and total intracranial brain control (TIBC) were analysed via Kaplan-Meier method. Cox proportional hazards models were used to identify prognostic factors. Results: Median follow-up was 20.8 months. One-year TIBC was 61.6% and one-year LC was 98.6%. Median OS was 28.7 (95%-CI: 16.9-40.5) months. In total, local progression (median time not reached) occurred in 2.0% and in 20.4% radiation-induced contrast enhancements (RICE) of the cavity (after median of 14.3 months) were diagnosed. Absence of extracranial metastases was identified as an independent prognostic factor for superior OS (p = <0.001) in multivariate analyses, while a higher Karnofsky performance score (KPS) was predictive for longer OS in univariate analysis (p = 0.041). Leptomeningeal disease (LMD) developed in 13% of patients. Conclusion: FSRT after surgical resection of BM is an effective and safe treatment approach with excellent local control and acceptable toxicity. Further prospective randomized trials are needed to establish standardized therapeutic guidelines.

Stereotactic ultrahypofractionated MR-guided radiotherapy for localized prostate cancer - Acute toxicity and patient-reported outcomes in the prospective, multicenter SMILE phase II trial.

Background: Due to superior image quality and daily adaptive planning, MR-guided stereotactic body radiation therapy (MRgSBRT) has the potential to further widen the therapeutic window in radiotherapy of localized prostate cancer. This study reports on acute toxicity rates and patient-reported outcomes after MR-guided adaptive ultrahypofractionated radiotherapy for localized prostate cancer within the prospective, multicenter phase II SMILE trial. Materials and methods: A total of 69 patients with localized prostate cancer underwent MRgSBRT with daily online plan adaptation. Inclusion criteria comprised a tumor stage ≤ T3a, serum PSA value ≤ 20 ng/ml, ISUP Grade group ≤ 4. A dose of 37.5 Gy was prescribed to the PTV in five fractions on alternating days with an optional simultaneous boost of 40 Gy to the dominant intraprostatic lesion defined by multiparametric MRI. Acute genitourinary (GU-) and gastrointestinal (GI-) toxicity, as defined by CTCAE v. 5.0 and RTOG as well as patient-reported outcomes according to EORTC QLQ-C30 and -PR25 scores were analyzed at completion of radiotherapy, 6 and 12 weeks after radiotherapy and compared to baseline symptoms. Results: There were no toxicity-related treatment discontinuations. At the 12-week follow-up visit, no grade 3 + toxicities were reported according to CTCAE. Up until the 12-week visit, in total 16 patients (23 %) experienced a grade 2 GU or GI toxicity. Toxicity rates peaked at the end of radiation therapy and subsided within the 12-week follow-up period. At the 12-week follow-up visit, no residual grade 2 GU toxicities were reported and 1 patient (1 %) had residual grade 2 enteritic symptoms. With exception to a significant improvement in the emotional functioning score following MRgSBRT, no clinically meaningful changes in the global health status nor in relevant subscores were reported. Conclusion: Daily online-adaptive MRgSBRT for localized prostate cancer resulted in an excellent overall toxicity profile without any major negative impact on quality of life.

Whole-pelvic irradiation with boost to involved nodes and prostate in node-positive prostate cancer-long-term data from the prospective PLATIN-2 trial.

PURPOSE: Node-positive prostate cancer is a potentially curable disease. Definitive radiotherapy to the prostate and lymphatic drainage is an effective treatment option but prospective long-term outcome data are scarce. Thus, the current study aimed to evaluate the toxicity and efficacy of definitive radiation therapy for men with prostate cancer and nodal metastases using modern irradiation techniques. METHODS: A total of 40 treatment-naïve men with node-positive prostate cancer were allocated to the trial. All patients received definitive radiation therapy at two German university hospitals between 2009 and 2018. Radiation was delivered as intensity-modulated radiation therapy (IMRT) with 51 Gy to the lymphatic drainage with simultaneous integrated boost (SIB) up to 61.2 Gy to involved nodes and 76.5 Gy to the prostate in 34 fractions. Feasibility and safety, overall and progression-free survival, toxicity, and quality of life measurements were analyzed. RESULTS: During a median follow-up of 79 months, median overall survival was 107 months and progression-free survival was 78 months. Based on imaging follow-up, no infield relapse was reported during the first 24 months of follow-up. There were 3 (8%) potentially treatment-related grade 3 toxicities. Common iliac node involvement was associated with a higher risk of progression (HR 15.8; 95% CI 2.1-119.8; p = 0.007). CONCLUSION: Definitive radiation to the lymphatic drainage with SIB to the involved nodes and prostate is a safe and effective treatment approach for patients with treatment-naïve, node-positive prostate cancer with excellent infield tumor control rates and tolerable toxicity. Location rather than number of involved nodes is a major risk factor for progression.

Detecting and Locating the Site of Local Relapse Using 18F-PSMA-1007 Imaging After Primary Treatment of 135 Prostate Cancer Patients-Potential Impact on PSMA-Guided Radiation Therapy.

PURPOSE: Due to limited imaging options, the visualization of a local relapse of prostate cancer used to pose a considerable challenge. However, since the integration of 18F-PSMA-1007-PET/CT into the clinic, a relapsed tumor can now easily be detected by hybrid imaging. The present study aimed to evaluate and map the allocate relapse in a large cohort of prostate cancer patients focusing on individual patient management conclusions for radiation therapy. PROCEDURES: The current study included 135 men with prostate cancer after primary treatment who underwent 18F-PSMA-1007-PET/CT due to biochemical relapse detecting a local relapse. Imaging data were reassessed and analyzed with regard to relapse locations. For the correlation of tumor foci with clinical data, we used binary logistic regression models as well as the Kruskal-Wallis test and Mann-Whitney test. RESULTS: In total, 69.6% of all patients (mean age: 65 years) underwent prostatectomy while 30.4% underwent radiation therapy. PET imaging detected most frequently a unifocal relapse (72.6%). There was a statistically significantly higher rate of ipsilateral cases among the relapsed tumors. Comparing both treatment approaches, tumors relapsed most commonly within the posterior region after surgery and transition/peripheral zone after radiation therapy, respectively. CONCLUSIONS: The present study confirms that 18F-PSMA-1007-PET/CT is highly suitable for the localization and allocation of a local relapse in patients with prostate cancer. The data enable further optimizing dose prescriptions and target volume delineations of radiation therapy in the future.

[Imaging of oligometastatic disease in selected urologic cancers]. / Bildgebung oligometastasierter Tumoren des Harntraktes.

BACKGROUND: Local treatment of the primary or metastatic sites in urologic malignancies is promising when compared to systemic therapy alone, leading to the definition of a potentially curative oligometastatic state. OBJECTIVES: Comparison of imaging modalities regarding local and metastatic tumor sites in urologic cancers. METHODS: Review of comparative trials addressing quality criteria of imaging modalities. RESULTS: Depending on primary tumor and metastatic site, conventional imaging modalities such as computer tomography (CT) and bone scintigraphy still represent the standard of care in Germany. Due to superior quality criteria, hybrid-imaging techniques were widely adopted for oncological staging and particular due to the new PSMA-ligand (PSMA-PET/CT) in prostate cancer imaging. The development of new radioisotopes as well as their clinical application remains a focus of current research. CONCLUSIONS: High-quality diagnostic imaging modalities lay the groundwork for a precise definition of an oligometastatic state. By enabling treatment of the entire tumor burden, a delay of systemic therapy, longer progression-free survival, or even curative treatment may become achievable.

[Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice]. / Künstliche Intelligenz und Smartphone-Programm-Applikationen (Apps) : Bedeutung für die dermatologische Praxis.

ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI): With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer. REQUIREMENTS AND POSSIBLE USE OF SMARTPHONE PROGRAM APPLICATIONS: Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.

Quantification and characterization of granulocyte macrophage colony-stimulating factor activated human peripheral blood mononuclear cells by fluorine-19 cellular MRI in an immunocompromised mouse model.

PURPOSE: The purpose of this study was to test fluorine-19 (19F) cellular magnetic resonance (MRI) as a non-invasive imaging modality to track therapeutic cell migration as a surrogate marker of immunotherapeutic effectiveness. MATERIALS AND METHODS: Human peripheral blood mononuclear cell- (PBMC)-derived antigen presenting cell (APC) were labeled with a 19F-perfluorocarbon (PFC) and/or activated with granulocyte macrophage colony-stimulating factor (GM-CSF). Viability, phenotype and cell lineage characterization preceded 19F cellular MRI of PFC+ PBMC under both pre-clinical 9.4 Tesla (T) and clinical 3T conditions in a mouse model. RESULTS: A high proportion of PBMC incorporated PFC without affecting viability, phenotype or cell lineage composition. PFC+ PBMC were in vivo migration-competent to draining and downstream lymph nodes. GM-CSF addition to culture increased PBMC migration to, and persistence within, secondary lymphoid organs. CONCLUSION: 19F cellular MRI is a non-invasive imaging technique capable of detecting and quantifying in vivo cell migration in conjunction with an established APC-based immunotherapy model. 19F cellular MRI can function as a surrogate marker for assessing and improving upon the therapeutic benefit that this immunotherapy provides.

Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.

BACKGROUND: Convolutional neural networks (CNNs) efficiently differentiate skin lesions by image analysis. Studies comparing a market-approved CNN in a broad range of diagnoses to dermatologists working under less artificial conditions are lacking. MATERIALS AND METHODS: One hundred cases of pigmented/non-pigmented skin cancers and benign lesions were used for a two-level reader study in 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Additionally, dermoscopic images were classified by a CNN approved for the European market as a medical device (Moleanalyzer Pro, FotoFinder Systems, Bad Birnbach, Germany). Primary endpoints were the sensitivity and specificity of the CNN's dichotomous classification in comparison with the dermatologists' management decisions. Secondary endpoints included the dermatologists' diagnostic decisions, their performance according to their level of experience, and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC). RESULTS: The CNN revealed a sensitivity, specificity, and ROC AUC with corresponding 95% confidence intervals (CI) of 95.0% (95% CI 83.5% to 98.6%), 76.7% (95% CI 64.6% to 85.6%), and 0.918 (95% CI 0.866-0.970), respectively. In level I, the dermatologists' management decisions showed a mean sensitivity and specificity of 89.0% (95% CI 87.4% to 90.6%) and 80.7% (95% CI 78.8% to 82.6%). With level II information, the sensitivity significantly improved to 94.1% (95% CI 93.1% to 95.1%; P < 0.001), while the specificity remained unchanged at 80.4% (95% CI 78.4% to 82.4%; P = 0.97). When fixing the CNN's specificity at the mean specificity of the dermatologists' management decision in level II (80.4%), the CNN's sensitivity was almost equal to that of human raters, at 95% (95% CI 83.5% to 98.6%) versus 94.1% (95% CI 93.1% to 95.1%); P = 0.1. In contrast, dermatologists were outperformed by the CNN in their level I management decisions and level I and II diagnostic decisions. More experienced dermatologists frequently surpassed the CNN's performance. CONCLUSIONS: Under less artificial conditions and in a broader spectrum of diagnoses, the CNN and most dermatologists performed on the same level. Dermatologists are trained to integrate information from a range of sources rendering comparative studies that are solely based on one single case image inadequate.

Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.

BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not been investigated. OBJECTIVE: To assess the diagnostic performance of a CNN when used to differentiate melanomas from combined naevi in comparison with dermatologists. METHODS: In this study, a CNN with regulatory approval for the European market (Moleanalyzer-Pro, FotoFinder Systems GmbH, Bad Birnbach, Germany) was used. We attained a dichotomous classification (benign, malignant) in dermoscopic images of 36 combined naevi and 36 melanomas with a mean Breslow thickness of 1.3 mm. Primary outcome measures were the CNN's sensitivity, specificity and the diagnostic odds ratio (DOR) in comparison with 11 dermatologists with different levels of experience. RESULTS: The CNN revealed a sensitivity, specificity and DOR of 97.1% (95% CI [82.7-99.6]), 78.8% (95% CI [62.8-89.1.3]) and 34 (95% CI [4.8-239]), respectively. Dermatologists showed a lower mean sensitivity, specificity and DOR of 90.6% (95% CI [84.1-94.7]; P = 0.092), 71.0% (95% CI [62.6-78.1]; P = 0.256) and 24 (95% CI [11.6-48.4]; P = 0.1114). Under the assumption that dermatologists use the CNN to verify their (initial) melanoma diagnosis, dermatologists achieve an increased specificity of 90.3% (95% CI [79.8-95.6]) at an almost unchanged sensitivity. The largest benefit was observed in 'beginners', who performed worst without CNN verification (DOR = 12) but best with CNN verification (DOR = 98). CONCLUSION: The tested CNN more accurately classified combined naevi and melanomas in comparison with trained dermatologists. Their diagnostic performance could be improved if the CNN was used to confirm/overrule an initial melanoma diagnosis. Application of a CNN may therefore be of benefit to clinicians.

[Dermoscopy in special locations : Nails, acral skin, face, and mucosa]. / Dermatoskopie in Sonderlokalisationen : Nägel, akrale Haut, Gesicht und Mukosa.

The use of dermoscopy by dermatologists across Europe has become a standard examination for benign and malignant skin lesions and increasingly also for inflammatory skin diseases. However, based on the experience of the authors from numerous dermoscopy courses, knowledge about important dermoscopic features in special locations such as mucosa or nails is often limited. This may be explained by (1) a different anatomy of the skin and its adnexa in special locations in comparison to the remaining integument, (2) difficult technical access to special locations with a dermatoscope, and (3) a rather low incidence of malignant skin neoplasms in areas of special locations (with the exception of facial skin/scalp). This article aims at explaining dermoscopic characteristics and features of important benign and malignant lesions of nails, acral skin, face, and mucosa.

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

Background: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking. Methods: Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

Diagnostic accuracy of dermatofluoroscopy in cutaneous melanoma detection: results of a prospective multicentre clinical study in 476 pigmented lesions.

BACKGROUND: Early detection is a key factor in improving survival from melanoma. Today, the clinical diagnosis of cutaneous melanoma is based mostly on visual inspection and dermoscopy. Preclinical studies in freshly excised or paraffin-embedded tissue have shown that the melanin fluorescence spectra after stepwise two-photon excitation, a process termed dermatofluoroscopy, differ between cutaneous melanoma and melanocytic naevi. However, confirmation from a larger prospective clinical study is lacking. OBJECTIVES: The primary end point of this study was to determine the diagnostic accuracy of dermatofluoroscopy in melanoma detection. Secondary end points included the collection of data for improving the computer algorithm that classifies skin lesions based on melanin fluorescence and the assessment of safety aspects. METHODS: This was a prospective, blinded, multicentre clinical study in patients with pigmented skin lesions (PSLs) indicated for excision either to rule out or to confirm cutaneous melanoma. All included lesions underwent dermoscopy and dermatofluoroscopy in vivo before lesions were excised and subjected to histopathological examination. RESULTS: In total, 369 patients and 476 PSLs were included in the final analysis. In 101 of 476 lesions (21·2%) histopathology revealed melanoma. The observed sensitivity of dermatofluoroscopy was 89·1% (90 of 101 melanomas identified), with an observed specificity of 44·8%. The positive and negative predictive values were 30·3% and 93·9%, respectively. No adverse events occurred. CONCLUSIONS: Dermatofluoroscopy is a safe and accurate diagnostic method to aid physicians in diagnosing cutaneous melanoma. Limitations arise from largely amelanotic or regressing lesions lacking sufficient melanin fluorescence.

Converting round tendons to flat tendon constructs: Does the preparation process have an influence on the structural properties?

PURPOSE: The structural properties of hamstring tendon grafts were evaluated in a porcine model, after processing it to a flat shape, to better replace or augment anatomic flat structures (e.g. ACL, MPFL or MCL). METHODS: In this biomechanical study, porcine flexor tendons were used which have a comparable shape to semitendinosus and gracilis tendons. One part of the tendon was prepared to a flat tendon construct by splitting the tendon longitudinally with a knife to half of the diameter of the tendon. The semi-split tendon was scratched out to a flat shape. The other matched part was tested in its original round shape. The tendons (n = 40) have been fixed in a uniaxial testing machine (Zwick/Roell) by cryo-clamps after preparing the fixed ends by 2-0 polyester sutures (2-0 Ethibond® EXCEL, Ethicon, Somerville, NJ). In every specimen, there was a free 60-mm tendon part between both clamps. The tendons have been loaded to failure to evaluate typical biomechanical parameters such as stiffness, yield load and maximum load. RESULTS: No statistically significant differences (n.s.) regarding stiffness, yield load and maximum load between natively round and processed flat tendons could be detected. CONCLUSION: A prepared flat-shaped tendon does not show any different structural properties compared with an original round tendon. Therefore, a flat tendon seems to be a biomechanical stable graft option for anatomic reconstruction or augmentation of injured natively flat-shaped structures such as MCL, MPFL or ACL.

Melanomas vs. nevi in high-risk patients under long-term monitoring with digital dermatoscopy: do melanomas and nevi already differ at baseline?

BACKGROUND: What lesions to select for a most efficient dermatoscopic monitoring of patients with multiple nevi remains an unresolved issue. OBJECTIVE: To compare the grade of atypia of melanomas and nevi of the same patient at baseline. METHODS: Prospective observational study using 236 dermatoscopic baseline images (59 quartets from 59 patients, each including one melanoma detected during follow-up and three nevi). Dermatologists (n = 26) were asked to assess the 'grade of dermatoscopic atypia' on a numerical scale and to identify the melanomas. RESULTS: On average, each dermatologist identified 24 of 59 melanomas (40%, range: 11-37). The number of correct picks was greater for dermatologists with moderate (mean: 28) or high (mean: 28) experience compared to beginners (mean 17; P < 0.001). In three of the 59 sets, none of the 26 dermatologists identified the melanoma. The mean grade of dermatoscopic atypia was 2.5 for nevi (95% CI: 2.4-2.6) and 3.0 for melanomas (95% CI: 2.9-3.1, P < 0.001). LIMITATIONS: Rating dermatologists were informed that each quartet of images included one melanoma creating substantial deviation from a real-life situation. CONCLUSION: A significant proportion of melanomas detected during follow-up cannot be differentiated from nevi at baseline. This necessitates the additional inclusion of less atypical lesions for monitoring.