Does medial-to-lateral femoral posterior condylar offset difference effect accuracy of established reference axes for determining femoral component rotation in total knee arthroplasty?

Aims and Objectives: The femoral posterior condylar offset (PCO) has been viewed with increased significance for knee joint movement patterns and has been discussed for its possible implication for femoral component rotation in total knee arthroplasty (TKA). However, a great inter-individual variability in medial and lateral PCO size has also been demonstrated. Though the medial and lateral PCO seem closely related to the functional flexion axis (fFA), determined by the radius curvature of the medial and lateral femoral condyle, the relationship of both parameters considering their impact on the accuracy of established reference axes for determining femoral component rotation in TKA remains unknown. The objective of this paper was, therefore, to compare the individual fFA with the anatomical and surgical transepicondylar axis (aTEA; sTEA) and with the posterior condylar axis (PCA) considering the medial and lateral PCO size. It was hypothesized that the disparity of the PCO influences the accuracy of the sTEA, aTEA, and PCA for determination of femoral component rotation in TKA. Materials and Methods: MRI investigations of 56 consecutive non-arthritic knee joints (male/female 28/28; mean age 22.8 years; range 16-59 years) were used for this study. Coronal, sagittal and transverse MRI images were used to measure the medial and lateral PCO and to determine the fFA, aTEA, sTEA, and PCA for each subject as described previously. A paired two-tailed t-test was used to test for differences between the medial and lateral PCO sizes. Deviation of the aTEA, sTEA and PCA from the fFA were analyzed with a one-sample t-test. Correlation analysis (Pearson r) was used to determine the relationship between the PCO ratio (medial-to-lateral PCO) and the deviation of the aTEA, sTEA and PCA from the fFA in each subject. The level of significance was set at 0.05. Results: The mean medial PCO was 34.0 mm (90%CI 28.72-30.55 mm; range 26.3 to 44.7 mm) and the lateral PCO averaged 29.64 mm (90%CI 30.3-31.4 mm; range 14.3 to 39.1 mm) (p<0.0001). The medial-to-lateral PCO ratio was 1.16 (90%CI 1.13 -1.19; range 0.93 to 1.85). The aTEA showed an increased external rotation in relation to the fFA throughout the whole PCO ratio range (mean deviation 4.2°; 95%CI 3.8°-4.6°; range -4.2° to 10.1°; p<0.0001), whereas the sTEA tends towards a slight but significant internal rotation throughout the PCO ratio range (mean deviation -1.6°; 95%CI -2.1°- -1.2°; range -8.1° to 4.8°; p<0.0001). The PCA showed the best conformity with the fFA (mean difference -0.2°; 95%CI -0.5°-0.2°; range -6° to 5.3°; p=0.36) and was most robust against medial-to-lateral PCO variations. A weak but significant positive correlation between the PCO ratio and the deviation from the fFA was solely found for the sTEA (r=0.28; p=0.042). Conclusion: Differences of the medial and lateral PCO size are a common finding. The PCA had the best match with the fFA, regardless of medial-to-lateral PCO disparity. Only the sTEA was influenced to a small extent by variation of the PCO-ratio

Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images

Marme F, Krieghoff-Henning E, Gerber B, et al. Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images. European Journal of Cancer. 2023;195: 113390.Background Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasingly omitted before therapy, and studies such as the randomised Intergroup Sentinel Mamma (INSEMA) trial address the potential for further de-escalation of axillary surgery. Therefore, it would be helpful to accurately predict the pretherapeutic sentinel status using medical images. Methods Using a ResNet 50 architecture pretrained on ImageNet and a previously successful strategy, we trained deep learning (DL)-based image analysis algorithms to predict sentinel status on hematoxylin/eosin-stained images of predominantly luminal, primary breast tumours from the INSEMA trial and three additional, independent cohorts (The Cancer Genome Atlas (TCGA) and cohorts from the University hospitals of Mannheim and Regensburg), and compared their performance with that of a logistic regression using clinical data only. Performance on an INSEMA hold-out set was investigated in a blinded manner. Results None of the generated image analysis algorithms yielded significantly better than random areas under the receiver operating characteristic curves on the test sets, including the hold-out test set from INSEMA. In contrast, the logistic regression fitted on the Mannheim cohort retained a better than random performance on INSEMA and Regensburg. Including the image analysis model output in the logistic regression did not improve performance further on INSEMA. Conclusions Employing DL-based image analysis on histological slides, we could not predict SLN status for unseen cases in the INSEMA trial and other predominantly luminal cohorts

Preexisting musculoskeletal burden and its development under letrozole treatment in early breast cancer patients

One of the most common adverse events (AEs) occurring during treatment with aromatase inhibitors (AIs) is musculoskeletal pain. The aim of our study was to analyze the influence of preexisting muscle/limb pain and joint pain on the development of AI-induced musculoskeletal AEs. Women eligible for upfront adjuvant endocrine therapy with letrozole were included in the PreFace study, a multicenter phase IV trial. During the first treatment year, they were asked to record musculoskeletal AEs monthly by answering questions regarding pain symptoms and rating the pain intensity on a numeric rating scale from 0 (no pain) to 10 (very strong pain). Pain values were compared using nonparametric statistical tests. Overall, 1,416 patients were evaluable. The average pain value over all time points in women with preexisting muscle/limb pain was 4.3 (median 4.3); in those without preexisting pain, it was 2.0 (median 1.7). In patients without preexisting muscle/limb pain, pain levels increased relatively strongly within the first 6 months (mean increase +0.9, p < 0.00001) in comparison with those with preexisting pain (mean increase +0.3, p < 0.001), resulting in a statistically significant difference (p < 0.00001) between the two groups. The development of joint pain was similar in the two groups. Women without preexisting muscle/limb pain or joint pain have the greatest increase in pain after the start of adjuvant AI therapy. Women with preexisting pain have significantly higher pain values. The main increase in pain values takes place during the first 6 months of treatment

Association between breast cancer risk factors and molecular type in postmenopausal patients with hormone receptor-positive early breast cancer

PurposeEvidence shows that genetic and non-genetic risk factors for breast cancer (BC) differ relative to the molecular subtype. This analysis aimed to investigate associations between epidemiological risk factors and immunohistochemical subtypes in a cohort of postmenopausal, hormone receptor-positive BC patients.MethodsThe prospective, single-arm, multicenter phase IV PreFace study (Evaluation of Predictive Factors Regarding the Effectivity of Aromatase Inhibitor Therapy) included 3529 postmenopausal patients with hormone receptor-positive early BC. Data on their epidemiological risk factors were obtained from patients' diaries and their medical histories. Data on estrogen receptor, progesterone receptor, and HER2 receptor status were obtained from pathology reports. Patients with incomplete information were excluded. Data were analyzed using conditional inference regression analysis, analysis of variance, and the chi-squared test.ResultsIn a cohort of 3392 patients, the strongest association with the molecular subtypes of BC was found for hormone replacement therapy (HRT) before diagnosis of early BC. The analysis showed that patients who took HRT at diagnosis had luminal A-like BC more often (83.7%) than those who had never taken HRT or had stopped taking it (75.5%). Luminal B-like BC and HER2-positive BC were diagnosed more often in women who had never taken HRT or had stopped taking it (13.3% and 11.2%, respectively) than in women who were taking HRT at diagnosis of BC (8.3% and 8.0%, respectively).ConclusionsThis analysis shows an association between HRT and the distribution of molecular subtypes of BC. However, no associations between other factors (e.g., age at diagnosis, body mass index, smoking status, age at menopause, number of deliveries, age at first delivery, breastfeeding history, or family history) were noted