Malignant pleural mesothelioma segmentation for photodynamic therapy planning

Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria

BACKGROUND

In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy.

METHODS

In a multicentre retrospective cohort study; 183 CT datasets were split into training and internal validation (123 datasets (80 fully annotated); 108 patients; 1 centre) and external validation (60 datasets (all fully annotated); 30 patients; 3 centres). Detailed manual annotations were used to train the CNN, which used two-dimensional U-Net architecture. CNN performance was evaluated using correlation, Bland-Altman and Dice agreement. Volumetric response/progression were defined as ≤30%/≥20% change and compared with modified Response Evaluation Criteria In Solid Tumours (mRECIST) by Cohen's kappa. Survival was assessed using Kaplan-Meier methodology.

RESULTS

Human and artificial intelligence (AI) volumes were strongly correlated (validation set r=0.851, p<0.0001). Agreement was strong (validation set mean bias +31 cm3 (p=0.182), 95% limits 345 to +407 cm3). Infrequent AI segmentation errors (4/60 validation cases) were associated with fissural tumour, contralateral pleural thickening and adjacent atelectasis. Human and AI volumetric responses agreed in 20/30 (67%) validation cases κ=0.439 (0.178 to 0.700). AI and mRECIST agreed in 16/30 (55%) validation cases κ=0.284 (0.026 to 0.543). Higher baseline tumour volume was associated with shorter survival.

CONCLUSION

We have developed and validated the first fully automated CNN for volumetric MPM segmentation. CNN performance may be further improved by enriching future training sets with morphologically challenging features. Volumetric response thresholds require further calibration in future studies.

Progress of Phototherapy Applications in the Treatment of Bone Cancer

Bone cancer including primary bone cancer and metastatic bone cancer, remains a challenge claiming millions of lives and affecting the life quality of survivors. Conventional treatments of bone cancer include wide surgical resection, radiotherapy, and chemotherapy. However, some bone cancer cells may remain or recur in the local area after resection, some are highly resistant to chemotherapy, and some are insensitive to radiotherapy. Phototherapy (PT) including photodynamic therapy (PDT) and photothermal therapy (PTT), is a clinically approved, minimally invasive, and highly selective treatment, and has been widely reported for cancer therapy. Under the irradiation of light of a specific wavelength, the photosensitizer (PS) in PDT can cause the increase of intracellular ROS and the photothermal agent (PTA) in PTT can induce photothermal conversion, leading to the tumoricidal effects. In this review, the progress of PT applications in the treatment of bone cancer has been outlined and summarized, and some envisioned challenges and future perspectives have been mentioned. This review provides the current state of the art regarding PDT and PTT in bone cancer and inspiration for future studies on PT.