Non-Professional Phagocytosis Increases in Melanoma Cells and Tissues with Increasing E-Cadherin Expression.

Non-professional phagocytosis in cancer has been increasingly studied in recent decades. In malignant melanoma metastasis, cell-in-cell structures have been described as a sign of cell cannibalism. To date, only low rates of cell-in-cell structures have been described in patients with malignant melanoma. To investigate these findings further, we examined twelve primary melanoma cell lines in both adherent and suspended co-incubation for evidence of engulfment. In addition, 88 malignant melanoma biopsies and 16 healthy tissue samples were evaluated. E-cadherin levels were determined in the cell lines and tissues. All primary melanoma cell lines were capable of phagocytosis, and phagocytosis increased when cells were in suspension during co-incubation. Cell-in-cell structures were also detected in most of the tissue samples. Early T stages and increasingly advanced N and M stages have correspondingly lower rates of cell-in-cell structures. Non-professional phagocytosis was also present in normal skin tissue. Non-professional phagocytosis appears to be a ubiquitous mechanism in malignant melanoma. The absence of phagocytosis in metastases may be one reason for the high rate of metastasis in malignant melanoma.

Impact of COVID-19 on Quality of Life in Long-Term Advanced Rectal Cancer Survivors.

Colorectal cancer remains one of the most commonly diagnosed cancers. Advanced rectal cancer patients receive neoadjuvant radiochemotherapy as well as surgery and suffer from reduced health-related quality of life due to various side effects. We were interested in the role of the COVID-19 pandemic and how it affected those patients' quality of life. A total of 489 advanced rectal cancer patients from the University Hospital Erlangen in Germany were surveyed between May 2010 and March 2022 and asked to fill out the EORTC QLQ-C30 and QLQ-CR38 questionnaires over eight different time points: at the beginning, during and after radiochemotherapy, right before surgery, and in yearly intervals after surgery for up to four years. Answers were converted to scores to compare the COVID-19 period to the time before March 2020, focusing on the follow-ups, the developments over time-including by sex and age-and the influence of the TNM cT-stage. Overall, a trend of impaired functional and symptom scores was found across all surveys with few significances (body image -10.6 percentage points (pp) after one year; defecation problems +13.5 pp, insomnia +10.2 pp and weight loss +9.8 pp after three years; defecation problems +11.3 pp after four years). cT4-stage patients lost significantly more weight than their cT1-3-stage counterparts (+10.7 to 13.7 pp). Further studies should be conducted to find possible causes and develop countermeasures for future major infectious diseases.

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy.

Background: Deep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated. Results: Blinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703). Conclusions: We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating.

Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design.

We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation. In the nonrigid-registration-based mapping method, LNs are mapped into a calculated template CT representing the cohort-average patient anatomy, and kernel density estimation is employed to estimate the underlying average 3D-LN probability distribution allowing for analysis and visualization without prespecified level definitions. Multireader assessment by three radio-oncologists with majority voting was used to evaluate the deep learning method and obtain the ground-truth distribution. For the mapping technique, the proportion of LNs predicted by the 3D probability distribution for each level was calculated and compared to the deep learning and ground-truth distributions. As determined by a multireader review with majority voting, the deep learning method correctly categorized all 449 LNs to their respective levels. Level 2 showed the highest LN involvement (59.0%). The level involvement predicted by the mapping technique was consistent with the ground-truth distribution (p for difference 0.915). Application of the proposed methods to multicenter cohorts with selected H/N tumor subtypes for informing optimal RT target volume design is promising.

Substantial Impairment of Quality of Life during COVID-19 Pandemic in Patients with Advanced Rectal Cancer.

The aim of this study was to analyze the quality of life of patients with advanced rectal cancer before and during the COVID-19 pandemic and to determine whether the pandemic affected patients' quality of life. The study included 389 patients and was performed from May 2010 to June 2021. The fifteen months from March 2020 to June 2021 were categorized as the COVID-19 period. Patients were surveyed using the QLQ-C30 and QLQ-CR38 questionnaires. The questionnaires were used at different phases of radiochemotherapy: prior to RCT (day 1), during RCT (day 14), at the end of RCT (day 35), and prior to mesorectal surgery (day 70). Scores were formed from the questions. In addition, scores were analyzed for different age groups (<64 and >64) and sexes (female and male). Overall, patients reported lower functional scores and higher symptom scores during the pandemic than before the pandemic. Although it had been expected that older and younger patients would differ clearly, there were only minor differences. The comparison between the two sexes showed very different scores, with female patients having lower functional scores and higher symptom scores than male patients before and especially during COVID-19. In conclusion, age does not play a major role in quality of life, but sex does play an important role in perception of functioning and symptoms. COVID-19 also had a major impact on patients' lives, as it was a very isolating and stressful time for everyone, especially cancer patients, which was reflected in worsening scores.

The Distribution of Pelvic Nodal Metastases in Prostate Cancer Reveals Potential to Advance and Personalize Pelvic Radiotherapy.

BACKGROUND: Traditional clinical target volume (CTV) definition for pelvic radiotherapy in prostate cancer consists of large volumes being treated with homogeneous doses without fully utilizing information on the probability of microscopic involvement to guide target volume design and prescription dose distribution. METHODS: We analyzed patterns of nodal involvement in 75 patients that received RT for pelvic and paraaortic lymph node metastases (LNs) from prostate cancer in regard to the new NRG-CTV recommendation. Non-rigid registration-based LN mapping and weighted three-dimensional kernel density estimation were used to visualize the average probability distribution for nodal metastases. As independent approach, the mean relative proportion of LNs observed for each level was determined manually and NRG and non-NRG levels were evaluated for frequency of involvement. Computer-automated distance measurements were used to compare LN distances in individual patients to the spatial proximity of nodal metastases at a cohort level. RESULTS: 34.7% of patients had pelvic LNs outside NRG-consensus, of which perirectal was most common (25.3% of all patients) followed by left common iliac nodes near the left psoas major (6.7%). A substantial portion of patients (13.3%) had nodes at the posterior edge of the NRG obturator level. Observer-independent mapping consistently visualized high-probability hotspots outside NRG-consensus in the perirectal and left common iliac regions. Affected nodes in individual patients occurred in highly significantly closer proximity than at cohort-level (mean distance, 6.6 cm vs. 8.7 cm, p < 0.001). CONCLUSIONS: Based on this analysis, the common iliac level should extend to the left psoas major and obturator levels should extend posteriorly 5 mm beyond the obturator internus. Incomplete coverage by the NRG-consensus was mostly because of perirectal involvement. We introduce three-dimensional kernel density estimation after non-rigid registration-based mapping for the analysis of recurrence data in radiotherapy. This technique provides an estimate of the underlying probability distribution of nodal involvement and may help in addressing institution- or subgroup-specific differences. Nodal metastases in individual patients occurred in highly significantly closer proximity than at a cohort-level, which supports that personalized target volumes could be reduced in size compared to a "one-size-fits-all" approach and is an important basis for further investigation into individualized field designs.

Volumetric Regression in Brain Metastases After Stereotactic Radiotherapy: Time Course, Predictors, and Significance.

BACKGROUND: There is insufficient understanding of the natural course of volumetric regression in brain metastases after stereotactic radiotherapy (SRT) and optimal volumetric criteria for the assessment of response and progression in radiotherapy clinical trials for brain metastases are currently unknown. METHODS: Volumetric analysis via whole-tumor segmentation in contrast-enhanced 1 mm³-isotropic T1-Mprage sequences before SRT and during follow-up. A total of 3,145 MRI studies of 419 brain metastases from 189 patients were segmented. Progression was defined using a volumetric extension of the RANO-BM criteria. A subset of 205 metastases without progression/radionecrosis during their entire follow-up of at least 3 months was used to study the natural course of volumetric regression after SRT. Predictors for volumetric regression were investigated. A second subset of 179 metastases was used to investigate the prognostic significance of volumetric response at 3 months (defined as ≥20% and ≥65% volume reduction, respectively) for subsequent local control. RESULTS: Median relative metastasis volume post-SRT was 66.9% at 6 weeks, 38.6% at 3 months, 17.7% at 6 months, 2.7% at 12 months and 0.0% at 24 months. Radioresistant histology and FSRT vs. SRS were associated with reduced tumor regression for all time points. In multivariate linear regression, radiosensitive histology (p=0.006) was the only significant predictor for metastasis regression at 3 months. Volumetric regression ≥20% at 3 months post-SRT was the only significant prognostic factor for subsequent control in multivariate analysis (HR 0.63, p=0.023), whereas regression ≥65% was no significant predictor. CONCLUSIONS: Volumetric regression post-SRT does not occur at a constant rate but is most pronounced in the first 6 weeks to 3 months. Despite decreasing over time, volumetric regression continues beyond 6 months post-radiotherapy and may lead to complete resolution of controlled lesions by 24 months. Radioresistant histology is associated with slower regression. We found that a cutoff of ≥20% regression for the volumetric definition of response at 3 months post-SRT was predictive for subsequent control whereas the currently proposed definition of ≥65% was not. These results have implications for standardized volumetric criteria in future radiotherapy trials for brain metastases.

FSRT vs. SRS in Brain Metastases-Differences in Local Control and Radiation Necrosis-A Volumetric Study.

Background: While the role of stereotactic radiotherapy for brain metastases is increasing, evidence on the comparative efficacy and safety of fractionated stereotactic radiotherapy (FSRT) and single-session radiosurgery (SRS) is scarce. Methods: Longitudinal volumetric analysis was performed in a consecutive cohort of 120 patients and 190 brain metastases (>0.065 cm3 in volume / > ~5 mm in diameter) treated exclusively with FSRT (n = 98) and SRS (n = 92), respectively. A total of 972 tumor segmentations was used, averaging 5.1 time points per metastasis. Progression was defined using a volumetric extension of the RANO-BM criteria. Local control and radionecrosis were compared for lesions treated with FSRT and SRS, respectively. Results: Metastases treated with FSRT were significantly larger at baseline (mean, 4.66 vs. 0.40 cm3, p < 0.001). Biologically effective dose (BED) for metastases (α/ß = 12, linear-quadratic-cubic model) was significantly associated with local control, whereas BED for normal brain (α/ß = 2, linear-quadratic model) was significantly associated with radionecrosis. Median time to local progression was 22.9 months in the FSRT group compared to 14.5 months in the SRS group (p = 0.022). Overall radionecrosis rate at 12 months was 3.4% for FSRT and 14.8% for SRS (p = 0.010). Radionecrosis °IV requiring resection with histologic proof of radiation necrosis also was significantly reduced in the FSRT group (FSRT 0.0% vs. SRS 3.9%, p = 0.041). In multivariate analysis, FSRT was associated with reduced risk of progression (HR 0.47, p = 0.015) and reduced risk of radionecrosis (HR 0.18, p = 0.045). Conclusions: This volumetric study provides initial evidence that the improvements in therapeutic ratio expected for FSRT in larger brain metastases, might equally extend into the domain of smaller metastases, traditionally less considered for fractionated treatment. FSRT might constitute an important tool to further increase local control and reduce radionecrosis risk in stereotactic radiotherapy for brain metastases, that should be assessed in randomized intervention trials.

Macrophages and dendritic cells as actors in the immune reaction of classical Hodgkin lymphoma.

BACKGROUND: The inflammatory infiltrate plays a pivotal role in classical Hodgkin lymphoma (cHL). Here, we focussed on the role of macrophages (MΦ) and dendritic cells (DC). METHODS: MΦ and DC infiltration was investigated in 106 cHL specimens using immunohistochemistry and cytokine expression was analyzed in a subset by real-time PCR. Human peripheral blood-derived monocytes, DC, MΦ stimulated with GM-CSF (MΦGM-CSF, pro-inflammatory MΦ-1-model) or M-CSF (MΦM-CSF, immunomodulatory MΦ-2-model) were incubated with cHL cell line (L1236, HDLM2) supernatants (SN). DC maturation or MΦ polarization were investigated by flow cytometry. Furthermore, the impact of DC or MΦ on cHL cell proliferation was analyzed by BrdU/CFSE assay. RESULTS: In cHL tissues mature myeloid (m)DC and MΦ predominated. High numbers of CD83+ mDC and low numbers of CD163+ MΦ were associated with improved disease specific survival. In numerous cHL specimens increased levels of both pro- and anti-inflammatory cytokines and of IL13 and GM-CSF were observed compared to reactive lymphadenopathies. Maturation of DC and induction and maintenance of an immunomodulatory MΦ phenotype were promoted by SN derived from cHL cell lines. TNFα neutralization in SN resulted in a significant inhibition of mDC maturation. DC and pro-inflammatory MΦ inhibited the proliferation of cHL cells. CONCLUSION: Adopting an immunomodulatory phenotype is a potential mechanism for how MΦ promote immune evasion in cHL. Mature DC, in contrast, might participate in antitumoral immunity.