Haematological and nutritional prognostic biomarkers for patients receiving CROSS or FLOT

Deep Learning Histology for Prediction of Lymph Node Metastases and Tumor Regression after Neoadjuvant FLOT Therapy of Gastroesophageal Adenocarcinoma

BACKGROUND

The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroesophageal cancer biopsies.

METHODS

This study included 78 patients from the University Hospital of Cologne and 59 patients from the University Hospital of Heidelberg used as external validation.

RESULTS

After surgical resection, 33 patients from Cologne (42.3%) were ypN0 and 45 patients (57.7%) were ypN+, while 23 patients from Heidelberg (39.0%) were ypN0 and 36 patients (61.0%) were ypN+ (p = 0.695). The neural network had an accuracy of 92.1% to predict lymph node metastasis and the area under the curve (AUC) was 0.726. A total of 43 patients from Cologne (55.1%) had less than 50% residual vital tumor (RVT) compared to 34 patients from Heidelberg (57.6%, p = 0.955). The model was able to predict tumor regression with an error of ±14.1% and an AUC of 0.648.

CONCLUSIONS

This study demonstrates that visual features extracted by deep learning from therapy-naïve biopsies of gastroesophageal adenocarcinomas correlate with positive lymph nodes and tumor regression. The results will be confirmed in prospective studies to achieve early allocation of patients to the most promising treatment.

Systemic inflammatory response markers for prediction of response to neoadjuvant chemotherapy in patients with advanced gastric cancer

BACKGROUND

Tumour development is greatly influenced by the systemic inflammatory response. Inflammatory factors, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphcyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), mirror the balance between systemic inflammation and anti-tumour response. The current investigation examined the predictive and prognostic value of NLR, PLR, and LMR in advanced gastric cancer (GC) patients.

METHODS

This study is a retrospective, observational analysis involving 105 GC patients treated with neoadjuvant chemotherapy (NAC). Thestudy population included patients who met the eligibility criteria.The relationship between NLR, PLR, LMR and demographic and clinical variables was assessed using theΧ2test. Survival data were analysed by Kaplan-Meier curves.

RESULTS

High NLR levels were associated with more advanced tumour stage.Higher risk of no tumour regression after NAC was observed if a high pretreatment level of NLR or PLR was found. All patients with an increase in NLR after NAC had a significantly higher risk of no tumor response.In groups high (no change), increase, decrease, and low (no change), NLR and PLR OS medians were: 33, 67, 78, and not reached-NR and 34, 29, 36, and NR, respectively. All patients had a significantly higher risk of death if NLR increased after NAC. An increase in post-NAC PLR level was associated with an increased risk of death only if the PLR baseline value was low.

CONCLUSION

NLR and PLR are promising predictive and prognostic factors in advanced GC patients treated with NAC.