Introduction: Nowadays chemotherapy in breast cancer patients is optionally applied neoadjuvant, which allows for testing of tumor response to the chemotherapeutical treatment in vivo, as well as allowing a greater number of patients to benefit from a subsequent breast-conserving surgery. Material and methods: We compared breast ultrasonography, mammography, and clinical examination (palpation) results with postoperative histopathological findings after neoadjuvant chemotherapy, aiming to determine the most accurate prediction of complete remission and tumor-free resection margins. To this end, clinical and imaging data of 184 patients (193 tumors) with confirmed diagnosis of breast cancer and neoadjuvant therapy were analyzed. Results: After chemotherapy, tumors could be assessed by palpation in 91.7%, by sonography in 99.5%, and by mammography in 84.5% (chi-square p < 0.0001) of cases. Although mammography proved more accurate in estimating the exact neoadjuvant tumor size than breast sonography in total numbers (136/163 (83.44%) vs. 142/192 (73.96%), n.s.), 29 tumors could be assessed solely by means of breast sonography. A sonographic measurement was feasible in 192 cases (99.48%) post-chemotherapy and in all cases prior to chemotherapy. Conclusions: We determined a superiority of mammography and breast sonography over clinical palpation in predicting neoadjuvant tumor size. However, neither examination method can predict either pCR or tumor margins with high confidence.

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas—vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%—but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.

Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples prior to the neuropathological analysis. CF samples showed no significant spectral deviations, appearance, or disappearance of peaks, but an intensity reduction during freezing and subsequent recovery during the thawing process. In contrast, FF induces sustained spectral alterations depending on molecular composition, albeit with good signal-to-noise ratio preservation. These observations are also reflected in the varying dual-class classifier performance, initially trained on native, unfixed samples: The Matthews correlation coefficient is 81.0% for CF and 58.6% for FF meningioma and dura mater. Training on spectral differences between original FF and pure formalin spectra substantially improves FF samples’ classifier performance (74.2%). CF is suitable for training global multiclass classifiers due to its consistent spectrum shape despite intensity reduction. FF introduces changes in peak relationships while preserving the signal-to-noise ratio, making it more suitable for dual-class classification, such as distinguishing between healthy and malignant tissues. Pure formalin spectrum subtraction represents a possible method for mathematical elimination of the FF influence. These findings enable retrospective analysis of processed samples, enhancing pathological work and expanding machine learning techniques.

Even though non-invasive prediction of endometriosis may seem technically feasible using sophisticated machine learning algorithms, a standard clinical use case for non-surgical diagnosis of endometriosis has not yet been established. In the present paper, we assess the potential of the inflammatory serum markers hepcidin, soluble urokinase-type plasminogen activator receptor (suPar), and interleukin-6 (IL-6) in a cohort of 87 patients. Hereby, 59 patients were histologically diagnosed with endometriosis, whereas other 28 patients served as our non-endometriosis control group. An initial exploratory univariate statistical analysis (Mann-Whitney test) revealed the diagnostic potential of different serum levels of suPar (p = 0.024) and IL-6 (p < 0.001) between both groups; the formation of a distinct training data set (n = 77) subsequently allowed to train a supervised machine learning analysis (tree classifier) employing serum levels of suPar, hepcidin, and IL-6 as predictor variables. Based on an internal 5-fold cross validation, the classifier performance was initially assessed using standard metrics such as sensitivity, positive predictive value, and AUROC curve. Additionally, the algorithm was tested on an external validation (holdout) data set (n = 10), showing sufficient overall accuracy of 80% without tendencies of overfitting. In conclusion, our data demonstrates the diagnostic potential of IL-6 and suPar as pro-inflammatory serum biomarkers in endometriosis. Using a decision tree-based supervised learning approach, we additionally present a straight-forward way of a potential clinical employment, aiming at less invasive (non-surgical) diagnosis.

Background: Enhanced Recovery After Surgery (ERAS) protocols improve postoperative outcomes and promote multidisciplinary, evidence-based perioperative care. However, ERAS adoption in gynecological departments remains inconsistent, and the underlying implementation challenges are poorly understood. Objective: To identify key barriers, facilitators, and preferred implementation strategies influencing ERAS adoption in German gynecological departments, and to assess whether clinicians’ ERAS knowledge or institutional certification shapes these perceptions. Methods: We conducted a nationwide, web-based cross-sectional survey of gynecologic clinicians in Germany. The questionnaire assessed ERAS-related knowledge, current implementation status, and perceived barriers, facilitators, and strategies. Statistical analyses included equality of proportions tests, logistic regression, and internal consistency measurement. Results: A total of 116 clinicians participated; 66 provided data on barriers and 64 on facilitators and strategies. Only 37.9% reported routine ERAS use. The most frequently identified barriers were limited ERAS knowledge (40.9% “very important”) and insufficient personnel resources (40.9%). The strongest facilitators were improved patient well-being, reduced morbidity, and higher patient satisfaction (each >60% “very important”). High-impact implementation strategies included informational materials, workshops, and online training. Well-informed clinicians had significantly higher odds of reporting a positive professional impact of ERAS (OR = 9.0, p = 0.001). Conclusions: ERAS implementation in gynecological settings remains restricted by staff knowledge gaps and personnel limitations. Patient-centered benefits and interactive educational strategies serve as powerful facilitators. Enhanced staff education and multidisciplinary support structures may substantially improve ERAS uptake and contribute to greater professional satisfaction among clinicians.

Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF₂ slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue.