Statistical analysis of a lung cancer spectral histopathology (SHP) data set

Abstract

We report results on a statistical analysis of an infrared spectral dataset comprising a total of 388 lung biopsies from 374 patients. The method of correlating classical and spectral results and analyzing the resulting data has been referred to as spectral histopathology (SHP) in the past. Here, we show that standard bio-statistical procedures, such as strict separation of training and blinded test sets, result in a balanced accuracy of better than 95% for the distinction of normal, necrotic and cancerous tissues, and better than 90% balanced accuracy for the classification of small cell, squamous cell and adenocarcinomas. Preliminary results indicate that further sub-classification of adenocarcinomas should be feasible with similar accuracy once sufficiently large datasets have been collected.

Classification of malignant and benign tumors of the lung by infrared spectral histopathology (SHP)

“…The CIs were also obtained by analytical methods and agree very well with the simulations. 12 The results of this simulation also suggest that the annotation method described earlier that often yields hundreds or thousands of individual pixel spectra for each annotated spot produces a representative sampling of tissue homogeneity and patient-to-patient variance. This is in contrast to other cancer diagnostic methods that yield one data point per patient, whereas in SHP thousands of data points are created for each patient.…”

“…12 In the work described therein, efforts were reported that detailed the composition, in terms of patients and pixel spectra, of the training and validation data sets, the metrics used for the evaluation of the quality of the diagnostic results, the type of MLAs, the number of pixels per patient to be included in the training phase, the number of spectral features (data points per spectrum) included in the analysis, and a measure of the power and confidence interval of the classification. This Table 2 Number of pixel spectra, processed spectra, and annotated spectra in entire TMA data set Total spectra:…”

“…Increasing the number of spectra per class did not improve the classification accuracy. 12 The number of patients contributing to the 2000 spectra was varied from 30 to 135 but the size of the training set was held constant at 10 000. The results of this simulation are shown in Figure 5.…”

“…10,11 Furthermore, a detailed analysis of the biostatistical significance of the results was carried out, and their confidence intervals have been established. 12 Next, a short introduction to methods referred to in the past as 'optical diagnosis' will be presented. Optical and spectrometric methods are commonplace in histology and pathology.…”

An infrared spectroscopic blood test for non-small cell lung carcinoma and subtyping into pulmonary squamous cell carcinoma or adenocarcinoma

“…Using appropriate machine learning techniques, the sample can be classified into distinct groups. This has extensively been done on lung tissue samples [2,5,20], down to the sub-classification of adenocarcinoma subtypes [9].…”